{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b9439e3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:11.510268Z",
     "start_time": "2024-08-14T06:52:10.965805Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pywt\n",
    "import math\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9b95ca7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:41.421053Z",
     "start_time": "2024-08-14T06:52:40.435968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>设备状态</th>\n",
       "      <th>数据类型</th>\n",
       "      <th>定位时间</th>\n",
       "      <th>设备名称</th>\n",
       "      <th>设备编码</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>获取位置</th>\n",
       "      <th>警报状态</th>\n",
       "      <th>事件</th>\n",
       "      <th>电量</th>\n",
       "      <th>定位方式</th>\n",
       "      <th>方向</th>\n",
       "      <th>海拔高度</th>\n",
       "      <th>速度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8848</th>\n",
       "      <td>8849</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8849</th>\n",
       "      <td>8850</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8850</th>\n",
       "      <td>8851</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8851</th>\n",
       "      <td>8852</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8852</th>\n",
       "      <td>8853</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8853 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        序号                                          设备状态  数据类型  \\\n",
       "0        1  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "1        2  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "2        3  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "3        4  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "4        5  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "...    ...                                           ...   ...   \n",
       "8848  8849  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8849  8850  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8850  8851  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8851  8852  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8852  8853  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "\n",
       "                     定位时间          设备名称          设备编码          经度         纬度  \\\n",
       "0     2024-07-29 10:02:28  489076140280  489076140280  113.797464  22.688276   \n",
       "1     2024-07-29 10:02:58  489076140280  489076140280  113.797464  22.688276   \n",
       "2     2024-07-29 10:03:28  489076140280  489076140280  113.797464  22.688276   \n",
       "3     2024-07-29 10:03:58  489076140280  489076140280  113.797464  22.688276   \n",
       "4     2024-07-29 10:04:28  489076140280  489076140280  113.797464  22.688276   \n",
       "...                   ...           ...           ...         ...        ...   \n",
       "8848  2024-08-01 15:28:50  489076140280  489076140280  113.943112  22.688810   \n",
       "8849  2024-08-01 15:29:20  489076140280  489076140280  113.943112  22.688810   \n",
       "8850  2024-08-01 15:29:50  489076140280  489076140280  113.943112  22.688810   \n",
       "8851  2024-08-01 15:30:20  489076140280  489076140280  113.943112  22.688810   \n",
       "8852  2024-08-01 15:30:50  489076140280  489076140280  113.943112  22.688810   \n",
       "\n",
       "      获取位置    警报状态   事件   电量     定位方式   方向  海拔高度   速度  \n",
       "0     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "1     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "2     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "3     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "4     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "...    ...     ...  ...  ...      ...  ...   ...  ...  \n",
       "8848  获取定位     NaN  NaN  64%  GPS有效定位  252    69  0.0  \n",
       "8849  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8850  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8851  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8852  获取定位     NaN  NaN  64%  GPS有效定位  252    69  0.0  \n",
       "\n",
       "[8853 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel(\"D:/data/0280/0280.xlsx\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25443b9b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:41.994053Z",
     "start_time": "2024-08-14T06:52:41.979952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>设备状态</th>\n",
       "      <th>数据类型</th>\n",
       "      <th>定位时间</th>\n",
       "      <th>设备名称</th>\n",
       "      <th>设备编码</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>获取位置</th>\n",
       "      <th>警报状态</th>\n",
       "      <th>事件</th>\n",
       "      <th>电量</th>\n",
       "      <th>定位方式</th>\n",
       "      <th>方向</th>\n",
       "      <th>海拔高度</th>\n",
       "      <th>速度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>外壳破坏报警</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>326</td>\n",
       "      <td>74</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8576</th>\n",
       "      <td>8849</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8577</th>\n",
       "      <td>8850</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8578</th>\n",
       "      <td>8851</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8579</th>\n",
       "      <td>8852</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8580</th>\n",
       "      <td>8853</td>\n",
       "      <td>锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位</td>\n",
       "      <td>实时数据</td>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>489076140280</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>获取定位</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64%</td>\n",
       "      <td>GPS有效定位</td>\n",
       "      <td>252</td>\n",
       "      <td>69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8581 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        序号                                          设备状态  数据类型  \\\n",
       "0        1  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "1        2  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "2        3  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "3        4  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "4        5  锁杆开,无充电,解封,静止,短连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "...    ...                                           ...   ...   \n",
       "8576  8849  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8577  8850  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8578  8851  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8579  8852  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "8580  8853  锁杆开,无充电,解封,静止,长连接,东经,北纬,网络状态缺省,关闭,卡1,GPS有效定位  实时数据   \n",
       "\n",
       "                     定位时间          设备名称          设备编码          经度         纬度  \\\n",
       "0     2024-07-29 10:02:28  489076140280  489076140280  113.797464  22.688276   \n",
       "1     2024-07-29 10:02:58  489076140280  489076140280  113.797464  22.688276   \n",
       "2     2024-07-29 10:03:28  489076140280  489076140280  113.797464  22.688276   \n",
       "3     2024-07-29 10:03:58  489076140280  489076140280  113.797464  22.688276   \n",
       "4     2024-07-29 10:04:28  489076140280  489076140280  113.797464  22.688276   \n",
       "...                   ...           ...           ...         ...        ...   \n",
       "8576  2024-08-01 15:28:50  489076140280  489076140280  113.943112  22.688810   \n",
       "8577  2024-08-01 15:29:20  489076140280  489076140280  113.943112  22.688810   \n",
       "8578  2024-08-01 15:29:50  489076140280  489076140280  113.943112  22.688810   \n",
       "8579  2024-08-01 15:30:20  489076140280  489076140280  113.943112  22.688810   \n",
       "8580  2024-08-01 15:30:50  489076140280  489076140280  113.943112  22.688810   \n",
       "\n",
       "      获取位置    警报状态   事件   电量     定位方式   方向  海拔高度   速度  \n",
       "0     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "1     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "2     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "3     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "4     获取定位  外壳破坏报警  NaN  68%  GPS有效定位  326    74  0.0  \n",
       "...    ...     ...  ...  ...      ...  ...   ...  ...  \n",
       "8576  获取定位     NaN  NaN  64%  GPS有效定位  252    69  0.0  \n",
       "8577  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8578  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8579  获取定位     NaN  NaN  63%  GPS有效定位  252    69  0.0  \n",
       "8580  获取定位     NaN  NaN  64%  GPS有效定位  252    69  0.0  \n",
       "\n",
       "[8581 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除'A'列中值为0的行  \n",
    "data = data[data['定位方式'] != 'LBS']\n",
    "data\n",
    "# 重置索引列\n",
    "data = data.reset_index(drop=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6e60145",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:42.818903Z",
     "start_time": "2024-08-14T06:52:42.811893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>方向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8576</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8577</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8578</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8579</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8580</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8581 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度   方向\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276  326\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276  326\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276  326\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276  326\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276  326\n",
       "...                   ...         ...        ...  ...\n",
       "8576  2024-08-01 15:28:50  113.943112  22.688810  252\n",
       "8577  2024-08-01 15:29:20  113.943112  22.688810  252\n",
       "8578  2024-08-01 15:29:50  113.943112  22.688810  252\n",
       "8579  2024-08-01 15:30:20  113.943112  22.688810  252\n",
       "8580  2024-08-01 15:30:50  113.943112  22.688810  252\n",
       "\n",
       "[8581 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features=data[['定位时间','经度','纬度','方向']]\n",
    "# data_features=data_features.set_index('定位时间',inplace=False)\n",
    "data_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c06f585",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:51.971702Z",
     "start_time": "2024-08-14T06:52:51.964146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>方向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度   方向\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276  326\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276  326\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276  326\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276  326\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276  326\n",
       "...                   ...         ...        ...  ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810  252\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810  252\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810  252\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810  252\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810  252\n",
       "\n",
       "[8559 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除'A'列中值为0的行  \n",
    "data_features = data_features[data_features['经度'] != 0]\n",
    "data_features\n",
    "# 重置索引列\n",
    "data_features = data_features.reset_index(drop=True)\n",
    "data_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "acd67f38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:52.942284Z",
     "start_time": "2024-08-14T06:52:52.939221Z"
    }
   },
   "outputs": [],
   "source": [
    "data_features_1=data_features.copy()\n",
    "data_features_2=data_features.copy()\n",
    "data_features_3=data_features.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d3a8b0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:53.898168Z",
     "start_time": "2024-08-14T06:52:53.891381Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8559 entries, 0 to 8558\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   定位时间    8559 non-null   object \n",
      " 1   经度      8559 non-null   float64\n",
      " 2   纬度      8559 non-null   float64\n",
      " 3   方向      8559 non-null   int64  \n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 267.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f81a778",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-14T06:52:54.748967Z",
     "start_time": "2024-08-14T06:52:54.667990Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= data_features[['经度']]\n",
    "y =data_features[['纬度']]\n",
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572408a2",
   "metadata": {},
   "source": [
    "# 中值滤波"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c7968d47",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:19.843539Z",
     "start_time": "2024-08-07T01:17:17.247316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>方向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度   方向\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276  326\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276  326\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276  326\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276  326\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276  326\n",
       "...                   ...         ...        ...  ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810  252\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810  252\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810  252\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810  252\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810  252\n",
       "\n",
       "[8559 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 中值滤波\n",
    "long=len(data_features)\n",
    "\n",
    "\n",
    "step=2\n",
    "star_index=2\n",
    "end_index=long-step\n",
    "for i in range(star_index,end_index):\n",
    "    a=data_features.iloc[i-step:i+step+1].sort_values(by='经度')\n",
    "    data_features.at[i, '经度']=a.iloc[step][1]\n",
    "    b=data_features.iloc[i-step:i+step+1].sort_values(by='纬度')\n",
    "    data_features.at[i, '纬度']=b.iloc[step][2]\n",
    "\n",
    "data_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "14f831d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:19.950694Z",
     "start_time": "2024-08-07T01:17:19.844544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= data_features[['经度']]\n",
    "y =data_features[['纬度']]\n",
    "plt.figure(figsize=(5, 5), dpi=200)\n",
    "plt.plot(data_features_1['经度'], data_features_1['纬度'],label = '原始轨迹')\n",
    "plt.plot(x, y,label='中值滤波后轨迹')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fdf7bea4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:19.953515Z",
     "start_time": "2024-08-07T01:17:19.951703Z"
    }
   },
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "# data_features.to_excel(\"D:/data/0280zhongzhi.xlsx\",index=True,header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4532ed8",
   "metadata": {},
   "source": [
    "# 卡尔曼滤波"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "10be287f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:19.959151Z",
     "start_time": "2024-08-07T01:17:19.954520Z"
    }
   },
   "outputs": [],
   "source": [
    "# 卡尔曼滤波\n",
    "class KalmanFilter:  \n",
    "    def __init__(self, process_variance, measurement_variance,x,y): #构造方法\n",
    "        self.x = np.array([[x], [y]])  # 初始状态：经度和纬度  \n",
    "        self.P = np.eye(2) * 1.0  # 初始协方差矩阵  \n",
    "        self.A = np.eye(2)  # 状态转移矩阵  \n",
    "        self.Q = np.eye(2) * process_variance  # 过程噪声协方差矩阵  \n",
    "        self.H = np.eye(2)  # 观测矩阵  \n",
    "        self.R = np.eye(2) * measurement_variance  # 测量噪声协方差矩阵  \n",
    "  \n",
    "    def update(self, z):  \n",
    "        # 预测  \n",
    "        x_pred = np.dot(self.A, self.x)  \n",
    "        P_pred = np.dot(np.dot(self.A, self.P), self.A.T) + self.Q  #self.A.T为self.A的转置矩阵   （（A*P）*A-1）+Q\n",
    "  \n",
    "        # 更新  \n",
    "        S = np.dot(np.dot(self.H, P_pred), self.H.T) + self.R  \n",
    "        K = np.dot(np.dot(P_pred, self.H.T), np.linalg.inv(S))  #np.linalg.inv(S)计算S矩阵的逆矩阵\n",
    "        self.x = x_pred + np.dot(K, (z - np.dot(self.H, x_pred))) #更新经纬度的坐标，为下一次预测的初始状态\n",
    "        self.P = np.dot((np.eye(len(self.x)) - np.dot(K, self.H)), P_pred) #更新协方差矩阵\n",
    "  \n",
    "    def get_state(self):  \n",
    "        return self.x  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5a42c199",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.259964Z",
     "start_time": "2024-08-07T01:17:19.960156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>方向</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度   方向\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276  326\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276  326\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276  326\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276  326\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276  326\n",
       "...                   ...         ...        ...  ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810  252\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810  252\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810  252\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810  252\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810  252\n",
       "\n",
       "[8559 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用卡尔曼滤波器 \n",
    "kf = KalmanFilter(process_variance=0.01, measurement_variance=0.01,x=data_features_1['经度'][0],y=data_features_1['纬度'][0])  \n",
    "\n",
    "# 假设这是不准确的经纬度观测值  \n",
    "measurements = []  \n",
    "for index, row in data_features_1.iterrows():  \n",
    "    # 提取经度和纬度，并创建NumPy数组  \n",
    "    measurement = np.array([[row['经度']], [row['纬度']]])  \n",
    "    # 将NumPy数组添加到measurements列表中  \n",
    "    measurements.append(measurement)\n",
    "\n",
    "estimated_coords = []\n",
    "\n",
    "# 对每个观测值进行更新  \n",
    "for z in measurements:  \n",
    "    kf.update(z)\n",
    "    x = kf.get_state()[0, 0]  # 注意这里使用了numpy的索引方式，假设get_state()返回的是numpy数组  \n",
    "    y = kf.get_state()[1, 0]\n",
    "    estimated_coords.append({'经度': x, '纬度': y})   # 将估计的(x, y)坐标添加到列表中\n",
    "data_features_1[['经度','纬度']]=pd.DataFrame(estimated_coords)\n",
    "data_features_1\n",
    "\n",
    "\n",
    "\n",
    "# #可以结合两种算法（中值滤波+卡尔曼滤波器）中值滤波将异常数据滤除，卡尔曼滤波将数据变平滑\n",
    "# kf = KalmanFilter(process_variance=0.01, measurement_variance=0.1,x=data_features['经度'][0],y=data_features['纬度'][0])  \n",
    "\n",
    "# # 假设这是不准确的经纬度观测值  \n",
    "# measurements = []  \n",
    "# for index, row in data_features.iterrows():  \n",
    "#     # 提取经度和纬度，并创建NumPy数组  \n",
    "#     measurement = np.array([[row['经度']], [row['纬度']]])  \n",
    "#     # 将NumPy数组添加到measurements列表中  \n",
    "#     measurements.append(measurement)\n",
    "\n",
    "# estimated_coords = []\n",
    "\n",
    "# # 对每个观测值进行更新  \n",
    "# for z in measurements:  \n",
    "#     kf.update(z)\n",
    "#     x = kf.get_state()[0, 0]  # 注意这里使用了numpy的索引方式，假设get_state()返回的是numpy数组  \n",
    "#     y = kf.get_state()[1, 0]\n",
    "#     estimated_coords.append({'经度': x, '纬度': y})   # 将估计的(x, y)坐标添加到列表中\n",
    "# data_features[['经度','纬度']]=pd.DataFrame(estimated_coords)\n",
    "# data_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5092cc1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.395631Z",
     "start_time": "2024-08-07T01:17:20.261976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4cAAANVCAYAAADLG4uyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzdd3iV9f3/8dd9ZvZkBAh7KUtEEBy4ABdYZ+vWVqu1trWOitWq1Vpnsf21+q21Q3HUKlbRukERcTBEmbJ3AiEEAmTnrPv3RyBwn5OQBHLOfU7yfFzXucznc9/3OW96XU3yymcZpmmaAgAAAAC0aw67CwAAAAAA2I9wCAAAAAAgHAIAAAAACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMIhAAAAAECEQwAAAACACIcAAAAAABEOAQAAAAAiHAIAAAAARDgEAAAAAIhwCAAAAAAQ4RAAAAAAIMJhqzBNU6FQyO4yAAAAAOCwxTQchkIhBQKBFj0TDAZb/EysfPzxxxo3bpxSUlLkdDqVmpqq888/X3PnzrW7NAAAAABoEcM0TTOaHzB//nw9+uij+vzzz1VaWipJ6tu3r66//nrdfvvt8nq9Ec989NFHmjJlihYsWKCysjI5HA4NGjRIN998s2666SYZhnHEdW3atEm9e/du9v2//e1v9cADD9S3n3vuOf34xz+WaZpKT09X586dtWnTJgUCATmdTk2dOlVXXXXVEdd5sJqaGi1btkyS1LFjR7lcrlZ9fwAAAACJIRAIqKSkRJI0dOhQJSUlHfmbmlE0depU0+l0mpLM5ORkc8yYMWbfvn1NSaYkc+zYsWZtba3lmYceeqj+ekZGhnniiSea+fn59X2XXnppq9S2adMm0+v1NvkyDMOUZD744IP1z65atcpMTk42DcMw//CHP5g+n880TdMsLi42L7jgAlOSmZWVZZaUlLRKrfstWLCg/n8HXrx48eLFixcvXrx48ZJkLliwoFXyRtTC4eLFi02Px2NKMu+44w6zvLy8/tozzzxTH7oefvjh+v7333/flGQ6nU7z8ccfrw+OgUDAvPfee+v/8f/+97+jVbbF5s2bzeTkZNPtdpurV6+u77/oootMSeadd94Z8Ux1dbWZk5NjSjKnTp3aqvUQDnnx4sWLFy9evHjx4hX+aq1wGLVppWeddZZmzJihyZMn6/HHH4+4fskll+iNN97Q0UcfrRUrVsg0TQ0aNEirVq3SX//6V/30pz+NeGbUqFFauHChzjnnHL3//vvRKNvi8ssv16uvvqo77rhDU6ZMkSTt3r1bgwYNUnl5ubZu3arMzMyI50488UTNnTtXjzzyiO6+++5Wq+fgqbALFixQly5dWu29AQAAACSOoqIiHX/88ZKkjRs3qlevXkf8nlFZtFZZWamUlBSNGjVK999/f4P3jBo1Sm+88Ya2bt0qSSooKFCfPn3UvXt33XTTTQ0+M3LkSC1cuLD+mWiaN2+eXn31VXXq1Mnyb8jOzlZRUZHKy8uVnp7e4LOFhYWSpLy8vFat6eA1hl26dFF+fn6rvj8AAACAxNNae5FEJRympqZq+vTph7xn27ZtklQfsHr06KH33nuvRc9Ei2mauvXWWyVJjzzyiDIyMiLuaayG6dOnq6CgQB6PR+ecc040ywQAAACAVmPLOYfBYFBvvfWWJGn8+PHNeqasrEwzZ85s0TOH691339X8+fPVt29f/fCHP2zWM9XV1XrmmWd09dVXS5Iee+yxVh85BAAAAIBoseUshOeff15btmyRYRj1I3RNeeKJJ1RdXa3k5ORGp522lkcffVSSdOedd8rpdDZ5/6hRo7R8+XLV1NTI7Xbrqaee0s9//vMWf+7+6aiNKSoqavF7AgAAAEBzxDwcFhUV6a677pIkXXfddRo+fHiTzyxbtkx/+MMfJEn33HNPVEfkZs+erblz5yovL6/Zo4amaSoYDEqS/H6/5s2bpyuvvFLZ2dkt+uzu3bu3tFwAAAAAaBUxnVYaCoV01VVXqbS0VL1799aTTz7Z5DNVVVW67LLL5PP5NHr06PpgGS37Rw1vu+02eb3eZj2zcOFClZaW6tlnn1Vubq7+/e9/67zzzlNtbW00SwUAAACAVhPTkcP77rtPs2bNUlJSkqZNm9bgMRDhbrjhBq1YsUK5ubl69dVX5Xa7o1bf2rVrNWPGDLndbl1//fUtejYtLU033nijjjnmGJ1yyin68ssv9c9//lM/+9nPmv0eBQUFh7x+8Ha1AAAAANCaYhYOp02bpkceeUSS9Oyzz2rkyJFNPvPEE0/olVdekcvl0rRp01rl7I5Dee655yRJEydOVG5u7mG9x+jRo3Xqqadq5syZ+vjjj1sUDjmaAgAAAIBdYjKtdN68efXr9+6++25dc801TT4zffr0+gPkn376aZ1xxhnRLFHBYFAvvviiJNXvONqYnTt3asuWLY1e79+/vySpvLy89QoEAAAAgCiKejhcsWKFzjvvPFVXV+vSSy/Vww8/3OQzn332ma688kqFQiFNnjxZP/nJT6JdpmbMmKFt27YpKytLEydObPS+//73v+rYseMh79kfHLt27drqdQIAAABANEQ1HH733XcaN26cdu7cqdNOO00vvPCCDMM45DOzZ8/WpEmTVF1drSuuuEKPPfZYNEus9/rrr0uSzj777ENuRHP66acrKSlJy5cv19NPPx1xfd26dfrwww8lSePGjYtOsQAAAADQyqIWDouLizVu3Dht375dQ4cO1VtvvdXk7p/Lly/XpEmTVFFRofHjx2vq1KlNhsnWYJpmfaA7/fTTD3lvbm5u/RmGt912m+69914VFBTINE0tWLBA559/vgKBgAYNGqTLLrss6rUDAAAAQGuIWjicMmWKiouLJUkrV65U586dlZSU1OBrzpw5kqQHH3xQlZWVkuqmlqanpzf6zObNm+s/a/PmzfX9L730UotrXbRoUf0B802FQ0n6/e9/r0mTJikQCOjhhx9Wjx495HQ6NXr0aK1YsUIDBw7Uu+++2+yjMAAAAADAblHbrXTx4sX1XwcCAQUCgUbvDYVCEc/4/f5Dvr9pmpav958puP8w+pb44IMPJEndunWr30zmULxer/73v/9p6tSp+uc//6klS5YoGAxqwIABuvzyy3XLLbcoJSWlxXUAAAAAgF0M8+CUhbhWWFio7t27S6o7E5GjLwAAAID2KRrZICZHWQAAAAAA4hvhEAAAAABAOAQAAAAAEA4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAABDhEAAAAAAgwiEAAAAAQIRDAAAAAIAIhwAAAAAAEQ4BAAAAACIcAgAAAABEOAQAAAAAiHAIAAAAAJDksrsAAACiadGW3Zq7YZdyUjzq3zld/TqlKTPZbXdZAADEHcIhAKDNmrdhl674xzyFTGt/5wyv+ndKV//OafX/HdA5ndAIAGjXCIcAgDbrv98URgRDSSouq1VxWa2+WLezvs9hSBMGddaNp/TRcT1zYlglAADxgXAIAGiz1hSXN/vekCl99F2xPvquWMf1zNYNY/towqDOcjqMKFYIAED8IBwCANqkUMjUuh0Vlj7DkMwGRhLDfbN5t77Z/I16d0jV9Sf31iXH5SvJ7YxSpQAAxAfCIQCgTdq2t1pVvqClb86dp6vGH9TaHRVaW1yhNTvKta64Qht2VsgfjEyNG3dW6t63luuPM9fo6jE9dc0JPZWb5o3VPwEAgJgiHAIA2qS1YaOG6Uku5WcnyzAM9e+cLg09cK28xq/Xvi7Qc19s1La9NRHvVVrp058/Wau/fbZelxyXrx+P7aPeHVKj/U8AACCmOOcQANAmrQ1bb9i/U5oMo+H1g+lJbv14bB99Nvl0/fmy4RrUJaPB+2oDIf17/had8eRs/eSlhfpmc2mr1w0AgF0YOQQAtElri+tGDh1JhXKlrpGZlar/W7xYCgakimIp6JfMkCSz7r9mqG5BohnSuQNDGtOtVlt2Vai0slZ+06UCs6OqlFT//p8WS5++LnXNTNVVx4zXj48/3Z5/KAAArYRwCABok9buqJAzZYOSe/xThhHSOr+0bkkL3yRp30uStEYNrTbcJenPK/+naatP0ksXPq7OaZlHUjYAALZhWikAoM0xzbqdSt2ZX8swQjH5zKLQlzrztQv1zsoFMfk8AABaG+EQANDmbC+rUUVtQM7kgph+bshVorvn3ahfvPcXhUKxCaUAALQWppUCANqctcUVkqNKDu9OS//Yqmql7A9tDqfk9EhySIYkGZLhqDsMUcZB/5VUvWff+kQrU9KXKcmqdBz4W6vhCGr2zn/o1Je+1kvnPaleOZ2i8m8EAKC1EQ4BAG3OmuJyOZMLLX3eUEh/Li6Re3/HNf+T+pzavDfcvVma8wdp8SuSaT07sdDl1OSOHbQsyboicY8W63tvXaQ7j/2drj72jMP8lwAAEDtMKwUAtDnrdlRETCk92uc/EAxH/bj5wVCSsntK5z8t/WKhdMzldSOM++QHgnqhqFg/2lMW8Zjp3KvHl9yqa998WL5A4DD+JQAAxA7hEADQ5qzdUSFnkjUcDqmtrfsiq6c0/sHDe+OcPtKFf5Nuni8NuUT75qPKLen23Xv0t+07lBO0jiwahqlvy1/V2Jcu1fLtWw7vcwEAiAHCIQCgTTFNU2uKy+RMtgaxYbW+ui/O/z/Jm3ZkH9JxgHTJv6SffiUd/b367pOqa/TfrUUaXV0T8UiVY40uf//7emru20f22QAARAnhEADQppSU16oiWCLDVWXpH1JbKx1/o9R7bOt9WOdB0qUvST/5XBp4riSpYzCkv2/foV+W7pHTNK33O6v09zX36uLXfq3K/SOZAADECcIhAKBNWVNcoS7JSy192cGg8tPzpfEPROdDuwyTLv+PdMOnUr8Jckj68d4yTS0qVpcG1hquqXlPp/z7In21eVV06gEA4DAQDgEAbcq64j06MXWmpW9IrU/G+c9IntTofni3EdJV/5Wunyn1PkXDa316fWuRxlVWRdzqc27RjbOu0u8+fTm6NQEA0EyEQwBAm9JxxVQVJ1nD2NCOw6ReJ8WuiO7HS1e/JXUarMyQqT/t2Kl7d5bKHbJOMzUctXp9y+M6++WfaVdVeezqAwCgAYRDAEDbYZoatf3fWulxW7qHjrw59rU4nNKZv5NUt6fppeUV+s+27crxJ0fcujU4R+P+c5E2le6IcZEAABxAOAQAtBnm3kLtdJWr1mH98Ta0yyh7Cuo3Xup7Rn1zoN+v93cUqpcxOuLWoGu7bnz/t7GsDgAAC8IhAKDNKNuyTMu9Xktf19TuyvRm2lSRpAkPaf95iJKU6qvUO9lpuqT7XTJD1lq3+edpe/nuGBcIAEAdwiEAoM3YvWmRlno9lr7hnYbZVM0+eUOk4Vda+759Ub8dMlLPnPaiTNNZ3204AvrL3DdjXCAAAHUIhwCANiO4fYWWhY0cDus41KZqDnLGbyTXQWsNzaD08W81tvcg5RjHWG79ZOt7MS4OAIA6hEMAQJth7lmljW6XpW9ohzgIhxldpRN/Ye1b86G04TOd1+c8S3eVY62+LlwXw+IAAKhDOAQAtA3BgLaHimQaB9b3OeXQUTlH2VjUQU66RUrtaO2bca9uGnWOFEyxdP/f16/FsDAAAOoQDgEAbUPpBq3yOi1d3ZJ7y+P0NPJAjHnTpdPvsfZtX6r01e+qd7L1DMZFpZ8oFArFsDgAAAiHAIA2orxgacRmNEM7HWtTNY049hqpw0Br3ye/0zVHT7J0hVwlen35lzEsDAAAwiEAoI3Ys3FRxGY0Y7oNt6eYxjhd0oTfWfvKCnVR2SI5A50s3S8tfyOGhQEAQDgEALQRO0qWaqfLOq10eKdjGrnbRgPOknqNtXQ5vviTjs+0Ti3dXPOVymurY1kZAKCdIxwCANqETdUbLW2v6VbPjJ42VXMIhiGd+Xtrn69cdxo7rH3Oaj0z/53Y1QUAaPcIhwCAxOev1iZHuaWrm7unjIN2Lo0rXYdLwy61dPX/7nVlB61h9p2NhEMAQOwQDgEAia9klZaFbUbTN3eETcU00xn3Sc6D1kiGAvphwDqNdLe5VGt3FsW4MABAe0U4BAAkvLKCpVoRFg5H5Y+yqZpmyuounXCzpevSom9khA6smzSMkP4y//VYVwYAaKcIhwCAhLd805eqdlh/pJ3R+zibqmmBk2+TUnLrm6mmqVNrrbd8VfxRjIsCALRXhEMAQMJbtWeVpZ0Z8KpzakebqmmBpEzp1F9bui7fa51G6nNu0cfrlsSyKgBAO0U4BAAkvAK/dafPTsqzqZLDMPJHUk7f+ubo6hrlBEzLLf9YxNRSAED0EQ4BAImtqlSbXEFLV17a0TYVcxicbmnCgweaks6vsO68urJ8tnyBQGzrAgC0O4RDAEBCCxV/p7Uet6Wvf9c434wm3FGTpB4n1DfPq6i0XDadezV10cxYVwUAaGcIhwCAhLZx43ztdTotfSf1Otamag6TYUhn/r6+2d/v19G1Psstr696O9ZVAQDaGcIhACChLd+60NL2hAyN6NrHpmqOQP5IafBF9c3w0cOiwAIVV+yNdVUAgHaEcAgASGibKjZY2h2C6XKFjSQmjPG/lZx15zWeU1Epp3lgYxrD4ddT8960qzIAQDtAOAQAJC7T1NbQHktXrivfnlpaQ3YvaeT1kqQOoZBOqq6xXP644H0bigIAtBeEQwBA4irbqo1uw9KVnz3MpmJayUm31I8ehk8trTBWa9G2jXZUBQBoBwiHAICEVb1tqdaH7VQ6uPtom6ppJRldpeFXSJJOr6pSejBUf8kwTD29YJpdlQEA2jjCIQAgYX23/nP5DevI4el9jrGpmlZ00i8lwyGvKZ1VaR09/GbXTIVCoUYeBADg8BEOAQAJa9WOZZZ2esClHlkdbaqmFeX0kYZcIkn6XtjU0qCrWNNXzrOjKgBAG0c4BAAkrILqQku7g5ltUyVRMPZ2SdLwWp/y/X7LpReXvmFHRQCANo5wCABITMGACh3WUbVOSb1tKiYKOh0tHTVJhiJHDzdUf6HK2lp76gIAtFmEQwBAYirdoPVul6WrR8fjbComSsbeIUmaFBYO5azS375+14aCAABtGeEQAJCQSrZ8ra1h4XBkrwTfqTRctxFS3zPUPRDUiBrrmYf/W/8/m4oCALRVhEMAQEJasuFzS9thSmN7DbKpmijaN3oYfubhLnOJNpXusKMiAEAbRTgEACSkDaUrLO2cQJLSvck2VRNFPU+Suo/RmZVV8oTM+m7DCOov81+3sTAAQFtDOAQAJB7T1DZ/saUr19HFpmKizDCkU36ljJCp06uqLJfmFH1oU1EAgLaIcAgASDzl27XRbT0IvnN6G5xSul+/8VLesIhdS2udmzR7w3KbigIAtDWEQwBAwgltW6S1bo+lr3/X422qJgYMQxp7h06srlFOMGi59Pdvp9lUFACgrSEcAgASzsYNX6jcaf0RdnLPYTZVEyNHnydXbn9NDBs9XF72CWceAgBaBeEQAJBwVmz7xtL2hBwa0bWPTdXEiMMpjb09Ymqp6SzTE1+8alNRAIC2hHAIAEg4BdVbLO3sYLYcjnbwI23o93VUcpeIMw/f2fSqQqFQIw8BANA87eAnKQCgTancqS0O6zTKbG8bHzXcz+mWTrpF1+wtt3T7XYWa+u3HNhUFAGgrCIcAgMRStERrPG5LV/fcY20qxgbHXq1THRnq4fdbup9bPtWeegAAbQbhEACQUGq2fquNbms4HNF1iE3V2MCdJNcJP9dVYaOHe41l+nTDMpuKAgC0BYRDAEBCWbVpngKGYek7tVcb36k03Mgf6XsBlzLCjrWYMu8fNhUEAGgLCIcAgISyYe9qSzslkKTuWbk2VWMTb7pSR/9Ul5ZXWLq31M7R2p1FNhUFAEh0hEMAQOKo3qMtZpmlK93oZlMxNjv+Rv2gKiiXaR7ocwT10Jx/2VcTACChEQ4BAIlj+zKt9XgsXR3SBtlUjM1ScpR33HU6N+zcwyV73tGe6spGHgIAoHGEQwBA4ihaoi0ul6VrYO5Am4qJAyf8XFdVWI/1CDmr9OicV2wqCACQyAiHAICEEdy2WNvCwuHRHXvbVE0cSO+so4derjHV1Zbujwv/o0DYZjUAADSFcAgASBg7ihbL57DuVDq0czsOh5J00i91TVmVpcvnKtHfvn7fpoIAAImKcAgASAy+Sm2v2GLpMkIODezQ1aaC4kRWD53U/3vq6/NZuv+z4nmbCgIAJCrCIQAgMWxfrm0up6XLEcqRy+ls5IH2wzH2Dl2913qsRZlzrd5bvdCmigAAiYhwCABIDEVLItYbpjg621RMnOnQX+fkn66csHWGTy141qaCAACJiHAIAEgMRUu01W0Nh9lewuF+KadO1mVl5Za+bcH5Wr59SyNPAABgRTgEACSGoiXaGjatNC+lna83PFiXYbooe4S8oVB9l2mY+uOsx20sCgCQSAiHAID456+RSlZGTCvtndXdpoLiU+czfqPvVVRa+lbUfKaSXQU2VQQASCSEQwBA/Cv+TsFQICIcDsjtYVNBcar78Tq/83hLV6XT0LQ3rpYOGlFEy1XUBrS32m93GQAQVYRDAED82/ylSpxOBQzrGYfD8nrZU08cO+ai/9PIauuP93cd2+X/4k82VZTYtu2p1s/+/a2GPfCRRj/ysV5fyCgsgLaLcAgAiH+bvogYNTRDLg3IZc1hBJdXZwz9taWr0O3WZ/OmSBvn2FRU4qkNBPV/n67TuCc/03vLihQypRp/SL97d4VqA8Gm3wAAEhDhEAAQ34IBafNXEZvRuEK5cjj4MdaQK0+4VJmBXEvfi5lp0n+vk8q22VRV4pizpkTn/L/P9YePVqvabw2C5TUBfbetzKbKACC6+KkKAIhv25dIvvKIYyxSnR1tKij+ORwOTeh9vaVvUVKSlgb2Sq//UAqydq4hW/dU66aXvtE1zy3Qhp2Vjd737ebdMawKAGKHcAgAiG8bP5ekiGml2R7OODyUO8f+QEYg09L3Yka6VDBfmnm/TVXFpwNTSGfrw++2N3n/N4RDAG0U4RAAEN82fSFT0mKv19LdNa2bPfUkiBS3V2M6nm/pm5maUjc9d95fpeVv2lRZfPnmq5n67+M3qujjp2X6ayKuD++epR+f3NvSt3DzbpmmGasSASBmCIcAgPgV9Etb5mqt262NHrfl0vjeJ9hUVOK4/9TrZYY89e2QYejljPS6xv9+IZWstqky+20t3KIvp1yq42Zcoiv9/9Xv3c/rZc8jSleVJCkn1aPHLx6qN396oq4YbT0ypaS8VoW7q+0oGwCiinAIAIhfRUskX4VmpKZYuo1Ali4ZfKJNRSWO/Mwc9Us+w9L3Znqayg1D8lVIr10t1VbYVJ09amp9mvXyo0r9xxidVPGh5dooxxq97HlEN47M1qw7TtWlo3rI4TDUu0OqslOsf5z4dgtTSwG0PYRDAED82jhHpqSPwsJh/7ST2Km0mX5z8k9kmgfOh6xyOPRGelpdY+dq6Z1bpHYyRXLhlx9r0+Mn6Ix1jynLaHjDmWMcG3RPyWRlmQd2JDUMQ8f1zLbcx7pDAG0RP1kBAPFr0xda63ZrU9iU0ksHTbKpoMQzKr+fOjqOs/T9OzNd9fuVLn9DWvD3mNcVS1u3bdXsJ6/UiBmX6KjQuqYfKF4mPX+uVH5gc5oRhEMA7QDhEAAQn4J+acs8ppS2gpuGX2dpb3e5NPPg/10/ukcqWBDjqqKvxufXjJenKPnZ0Tqt/F05jMgR0qXZE1Rx+f+kjLANjnaulp4/R9pTIEk6roc1HK4sKlNlbSBqtQOAHQiHAID4tG2RTH8lU0pbwaXDxiop2MfS90JmuuqjUiggTbtWqiiJeW3RsnbJV1r72Mk6c91DyjHKI64XOrtr08T/aNgv/6u0gadKP3pfyrJuPKPSDXUjiKUbNSw/Sy7Hgem5IVNaUrAnyv8KAIgtfroCAOLTps+ZUtqKLu53haW9wuvVN0kHHQ9Svk164zopFIxxZa2sZq+2vnKL+rx5roaGVkVcrpZXS4+6TV1//Y16jTr3wIXsXtKPPpRy+lof2LtFev5cJZdt0OCuGZZLTC0F0NYQDgEA8Wnj50wpbUW3n3ixHIFcS9//Zedbb9o4R/r04RhW1YpMU1o6TTV/GqFua16Qs4EppCuyTpP/p/M17LIH5HB7I98js1vdCGLHo6z95duk58/R2Z1KLd3fsGMpgDaGcAgAiD8Bn0IF85lS2oo8LpdOybvI0rfQ69f6jDzrjZ8/KX30G6m8OIbVHaGS1dIL50lv3qCk2p0Rl4ucXbT5nBc16Na3ldG5dwNvcJD0POmH70l5Q639lSX68dqbdYLju/qubzfvVijUPnZ6BdA+8BMWABB/Nn6mdfIzpbSV3XvqtVIw6UCHId3bYbTkcFlvnPu09Odh0vuTpb1bY1tkS/hrpFkPy3zmJGnT5xGXa0y33sv9kXLu/EY9R5/f/PdN7SBd+47UzbrLq9tfrhfcj+kixxxJUllNQOtL2tc5kQDaNsIhACD+zHuGKaVR0DktU0enn2npWxZcpt8ffba2O53WmwM10oJnpb8Ml965Vdq9OWZ1NsvGOdLfTpLmPCEj5I+4/EnwWP196H90zs/+JG9SasvfPzlbuvotqccJlm6PEdQfPX/TLc43JZmsOwTQphAOAQDxpWS1zPWfMKU0Su4de4NM88D/joYjoNeqlurM7t10a6eOWpDklWWiZNAnffO89NQI6a2fSbvWx7xmi6pS6a2b66aR7oo8s7DQ7KAbfLdr/fh/6hcXj5fjoB1GWywpQ7p6unT0eRGXbnf/V1Pcz2rxxh2H//4AEGdcTd8CAEAMzf8bu5RG0bC8XurmOlHbgl9Y+k1D+iQ1WZ+kJquvz6/Lysp1XkWlUs19UTEUkBa/LC15RRpyiXTKr6SOA2NXuGlKS1+rO5OxalfE5TI59RfH6XrVdZzOPjFNJd7XdPMnhdpTs+fIPzvVlAYMkyqs6zC7+b/TiM2/kKo/kJKzjvxzAMBmhmmarKROEIWFherevbskqaCgQPn5+U08AQAJpqpU+uMgPZ3m0bPZmfXdRiBLi3/0GSOHraRg7w5d8t9fqsq1/JD3pYZCOr+8UpeVl6u3P/zAd0Ma9D1pzM+knN5Sco7kPIK/OZum5Kuse/krVVG5W59uXKwFxStVVrtbnSq+U3rtNklSSFJIhna4nCpwubTJ5VWZTX/uNkxT11Y69ctrZ8mVltv0AwDQSqKRDRg5BADEj29flBmo1kepWZZuppS2ru6ZnfTlD/+t3300S9PWvCZX5rcyHL6I+yodDr2Sma5XMtN1QnW1Li+r0ClV1apbnWhKK96ue0mSDCklR0rtVLehS2rHulfavv96M6Tq3XV/AKjaKVXu3PffXQpV7VRh7R4tdRta5vVqmdejVV6P/MZBU0KTJSVnRtRoN9MwNDUtpHn/OU9/uPgt9crpZHdJAHDYCIcAgPgQ9EsL/s6U0hhxOR363bnj9b1BI/TLaXO1w/xS7ux5cnpLGrx/bnKy5iYnq0sgoB4Ro4gH2yXV7JJqVkuRsz8jBAxpQ7Zbu50dD+8fEidWecp10fQLdf8JT+qCQWPsLgcADgvhEAAQH1a+I5Vt1Yws6+gQu5RG18heOfrwljP12/9105vfnihnyjq5c+bKlbZSRgMHyRe5XCpyxe+vD51SOik/LV/56fnqnt5dnVI6yWG08qizKX2w5AvNr/hQwYNGN/2uMt07/ybNK/yZHjvzhtb9TACIgfj97g4AaF/m/02mxC6lNkhPcuuPPxiu0wZ20m+mu1Ve2F+Ga7fc2fPlzlogh6vKlroM01RPf0jdA4ZkuFTmzNUOby+FDJcMGeqQkq0JAwapb1ZPdU/vrq5pXZXkSmr6jVvBBf0v0Aev7tbjlXO1y3XgGBDDEdS7257S0CX9deUxp8WkFgBoLYRDAID9Cr+RCuYzpdRm3zumq47rma3bXlusBRslX8nZ8u0cJ1fGUnmy58qZXBjVz3cGU5Tt6K1+mYM1Jv94nTNwlLpm5ET1M4/EOec/qWOfOlZ3Zbr0bdKBUGoYpv615GXCIYCEQzgEANjqu+ICLZ1xv7bkZOmL5GTLNaaUxl63rGT954YxenbOev1xxhoFQm4F9h6nwN7j5EjaKmdSgWQEj/hzDEPKy0hSj5xUHd2pq87qN0ojuvZJrFHi5Czljf2V/vnRPXo4N0dvZKTVXyoJLFFlba1SvV4bCwSAliEcAgBiJhAM6pMNSzVj/TwtLVmsYt8qma5SySkpMyPifqaU2sPpMHTzaf10cr8O+uWri7VxZ6UkKVTTTaGabof1nqkep47tka2RvbI1smeOhvfIUpq3DfwaMurHcs//m362p9ASDuWs0avLZuv6kWfZVhoAtFQb+K4MAIhnvkBA937ynL4s+lRloXWSs+bAxSZ+Cl0z9MLoFodDGpafpfduOVkPvbtC/1lQ0OznclI96pWbol4dUjWsW6ZG9srRUXnpcjnbYNB3eaUz7lPHN2/QkNpaLT9opPDddZ8QDgEkFMIhACCqfvDfu7W+9kPJkORs8nZJkhly6fjsH+j8QaOjWhualuJx6dGLhu3brGaZdlbUnYeYleJWr9zU+hDYu0NqXbtDqjKT3U28axsz5BLpq6d0atUmSzjcULlAoVCI0W8ACYNwCACIqvVVXzQZCtNCIR1TU6vhtbVKH/47nTnyYnVMi5xmCvucNThPpw7oqC2lVeqU7lVWisfukuKHwyGddrdOf/Ma/V92Vn13yLVLszYs0/h+x9hXGwC0AOEQABBVplEtI6zPCOSos2egrti7QCfuLVQ/v78uP/Y5XTrtRzZUieZIcjs1oHO63WXEp/4T1N+VobxAQNsPOgfyP8s/JBwCSBjMcwAARE0oFJLhsO5sedcxf9HS6z/TzDHf1492btTA/cFQksbcHPMagVbhdMsx+EKdVlVt6V5a+pVNBQFAyxEOAQBRU+33R/T1zs6T9hZKb/3UeiG3n9RvfIwqA6Jg6A8iwmGNY4PW7iyyqSAAaBnCIQAgaqr8tRF9yQpKr14hVZZYL5zws7q1W0Ci6n68Rno7KSUUOtBnSK8t/9S+mgCgBfgpDACImkpfTURfpy8fkYqWWDv7jpNGXBujqoAoMQx5h/5AI2usfxT5aitTSwEkBsIhACBqqgO+iL70dR9YO3L6Spf8S3I085wLIJ4N+4FGV1v/KFJS861NxQBAyxAOAQBR09DIods0DzS8GdLlr0rJ2TGsCoiijgN1jLebpavGVa6vC9fZVBAANB/hEAAQNdX+yJHDA+HQkC7+l9RxQGyLAqJs8LArlB207tL7xgrWHQKIf4RDAEDUVAWsa68M0zxwwO74B6QBZ8a6JCDqXAPP1PFhU0uXFn1mUzUA0HyEQwBA1NT4rNv6u03JkKShP5BO+qUtNQFR1/EoHeu3rqEtCa5S6OBdTAEgDhEOAQBRk7bsX5a2R6bU9Vjpe3+RDMOmqoAoMwwdlzvc0lXjrNWsDcvsqQcAmolwCACIjr1blb75PUuX05R06b8ld7I9NQExMmDgWeoSCFj63l41255iAKCZCIcAgOjYulB+mZauKjNdyuzWyANA2+Hoc1rEkRbrds61qRoAaB7CIQAgOsqK5AubOhpUkk3FADGW21eDg15L157gRpuKAYDmIRwCAKKjfJv8YV3Ggb1KgbbNMNQvZ5ilq8JVpS17SmwqCACaRjgEAERHAyOHhEO0J4OPOluekHVq9YdrvrapGgBoGuEQABAd5UXyh4VDh0E4RPuR3O8MHeXzWfrWrHvHpmoAoGmEQwBAdDQQDp2MHKI9ye6l3mHrDsvK5kqm2cgDAGAvwiEAoPWZplRWJH/YUYaMHKK9yck9zdLe7KqVNnxqTzEA0ATCIQCg9dWWSf7KiDWHTsNtU0GAPYYOv9bS3uZ2acenj9hUDQAcGuEQAND6yookSX6x5hDt29g+w+QMWX/dWrNrmbTpC5sqAoDGEQ4BAK2vfFvdfxzWHzMuRg7RziS5PfKYvSx9y7we6bPH7SkIAA6BcAgAaH37Rg7nJ1sPve+Y3MWOagBbdU0ZYGm/mpGuvZu/kLbMt6kiAGhYTMNhKBRSIBBo0TPBYLDFz9jBF7ZVNQC0a+XbVOx0aqXXY+me2PcMmwoC7HNev/GWdqnTqT/mZElznrCnIABoRNTD4fz583XBBRcoNzdXTqdTbrdb/fr106OPPqra2toGn/noo480YcIEZWZmyuVyyev1aujQoXrmmWdkttL2z5s2bZJhGM1+PfDAA5bna2pq9PDDD2vQoEHyer3yer1KSUnR9773PX3zzTetUiMAJKyyIs1JsY4aKpis7w892Z56ABv9aMQEZWm4pe/N9DR9Xfi5tJXfGQDEj6iGwxdeeEEnnXSS3n77bVVXV2vMmDHq27ev1q9fr3vuuUcTJkyIGHH7/e9/r7PPPlsff/yxJOnEE09U165dtXz5ct188826/PLLW6U2wzDqQ92hXsa+nfYcB62b2bNnj04++WTde++9Wrlypbp3764xY8bIMAy98847OvHEE/X222+3Sp0AkJDKt2tOcrKlq5P7GKW4vY08ALRdDodDT535kBSyjqT/LjdH1e/cJvkqbaoMAKyiFg6XLFmiG2+8UcFgUHfccYd27NihuXPnat26dXrmmWdkGIY+//xzTZkypf6ZDz74QPfdd5+cTqcef/xxlZSU6Msvv9SmTZt07733SpJee+01vfLKK0dcX8+ePVVTU3PI15o1a5SUlCS3263LLrus/tmf/OQn+uabb9SvXz/NmTNH69at09y5c7V27VqNGDFCPp9P1113ncrLy4+4TgBIRFVlWzUvbL3hKd1OsakawH7Du/TS6Z1+aOnb5HHrXzWbpDdvlEIhW+oCgINFLRxOnjxZPp9PkydP1pQpU5SWllZ/7aabbtJFF10kSXr55ZclSaZp6vbbb5ckPfXUU5o8ebI8nrq/sDmdTj300EMaOXKk5Zlou+uuu1RdXa1bbrlFAwbULSafO3eupk2bpg4dOmjWrFkaO3Zs/f1du3bVCy+8IEkqLS3VBx98EJM6ASDezK3arpqDdyo1DV07/Cz7CgLiwJSzfqqkYA9L37+yMrRu/UfSx/fbVBUAHBCVcFhZWamUlBSNGjVK99/f8De7UaNGSZK2bt0qSSooKFCfPn00YcIE3XTTTQ0+sz8c7n8mmubNm6dXX31VnTp1svwb1q5dqxNPPFH33XefunfvHvHckCFDlJSUFLM6ASDuBAP6ymVdMpAd7KZeOZ1sKgiIDx6XSw+d/JBkHvj1K2AYerBDjkJfPSUtfN7G6gBAisppxKmpqZo+ffoh79m2re4MrPT0dElSjx499N5777XomWgxTVO33nqrJOmRRx5RRkZG/bVrrrlG11xzTaPPlpaW1m+0E+06ASAehcojN6MZnD3GpmqA+HL2gBGauuR7+q7qrfq+xUle/Tc9TT947w4pq4fUb5x9BQJo12w55zAYDOqtt96SJI0fP/7QN+9TVlammTNntuiZw/Xuu+9q/vz56tu3r374wx+26Nk333xTpmnKMAyNG8c3dwDtz+yVn2u7y/q3x4uHnW9TNUD8+dukX8sR6GDp+1NOloockqZdKxWvsKcwAO1eVEYOm/L8889ry5YtMgyjfoSuKU888YSqq6uVnJzc6LTT1vLoo49Kku688045nc5mP1dbW6vHHntMknT++eerd+/eLfrcwsLCQ14vKipq0fsBgB0+2PCppd3ZH9IZfYbZVA0Qf7KSU/XzYb/WX1b8qr6vwuHQVV0768/FOzXklR9IP/5ESu9sY5UA2qOYh8OioiLdddddkqTrrrtOw4cPb/KZZcuW6Q9/+IMk6Z577lFeXl7U6ps9e7bmzp2rvLy8Fo8aPvDAA1q/fr2SkpL0xBMtP9i2oTWMAJBollWvkNwH2qP8SZbjgABIN4w6S6+vfktFwS/q+3a4XLq2S2c9sHOXznv5Iunq6VIaa3UBxE5Mf1qHQiFdddVVKi0tVe/evfXkk082+UxVVZUuu+wy+Xw+jR49uj5YRsv+UcPbbrtNXm/zz+OaNWtWfSB84okn1L9//6jUBwDxbP2u7drmKrP0jUk/yqZqgPj293MflCPQ0dLncxi6p1MHPekrVOD5s6U9BTZVB6A9iunI4X333adZs2YpKSlJ06ZNU2ZmZpPP3HDDDVqxYoVyc3P16quvyu12N/nM4Vq7dq1mzJght9ut66+/vtnPFRYW6vLLL1coFNLFF1+sX/ziF4f1+QUFh/4BUFRUpOOPP/6w3hsAYuGlb96WaRxoJ4dCOuO4S+0rCIhjvXI6afqF/9Hlb9+sKscay7WpWRlaW1Wmx54/W1nX/E/K7WtTlQDak5iFw2nTpumRRx6RJD377LP1x1IcyhNPPKFXXnlFLpdL06ZNU69evaJa43PPPSdJmjhxonJzc5v1THV1tS688ELt2LFDQ4cOrT/n8HDk5+cf9rMAEA8WF74vHbRUe0x1rdL7n2FfQUCc65PTWZ9e9You/+892uCbYbn2ZUqyrnb59cfnz1b/q9+SOg+2p0gA7UZMppXOmzevfv3e3XfffcijIPabPn267r77bknS008/rTPOiO4vF8FgUC+++KIk6eqrr27WM6FQSFdffbUWLlyovLw8vfPOO0pNTY1mmQAQt6r8tSowNln6RhidJG+aPQUBCSLF7dXblz+pc/J+YTkDUZI2edy6Jtej2S+fJxUutKlCAO1F1MPhihUrdN5556m6ulqXXnqpHn744Saf+eyzz3TllVcqFApp8uTJ+slPfhLtMjVjxgxt27ZNWVlZmjhxYrOe+cUvfqE33nhDKSkpeuedd9SzZ88oVwkA8WvG2kXyOUKWvtN6R/foIaAteeKsG/XrY/8sI5hi6a9wOHRrbor+8/r3pY2f21QdgPYgquHwu+++07hx47Rz506ddtppeuGFF2QYxiGfmT17tiZNmqTq6mpdccUV9UdDRNvrr78uSTr77LObtRHNL37xC/31r3+tn/LanGmyANCWzVs/29Lu6fer1xDONwRa4spjTtMLZ78qb6CbpT9oGHokJ01PvHOtAivft6k6AG1d1MJhcXGxxo0bp+3bt2vo0KF66623mgxdy5cv16RJk1RRUaHx48dr6tSpTYbJ1mCapj788ENJ0umnn97k/Q888ICefvppSXXrJ5s70ggAbVlhyVxLe6AvJHUdbk8xQAI7tmtvzbz8v+psRP7h+aXMVP3q01u099tXbKgMQFsXtXA4ZcoUFRcXS5JWrlypzp07KykpqcHXnDlzJEkPPvigKisrJdVNLU1PT2/0mc2bN9d/1ubNm+v7X3rppRbXumjRovoD5psKh7t3764/7kKSbr755kZrPPPMM1tcCwAkqpKgdcfl7q58yeFs5G4Ah5KdkqYPr/ynhqVcEHHtk9Rk3fT1A9r82V9iXxiANi1qu5UuXry4/utAIKBAINDovaFQKOIZv99/yPc3TdPydW1traS6jWVa6oMPPpAkdevWrcnzCVesWCGfz1ff3v+5DTn4PgBoy3ZV7tV2t0/SgdkeA7qeaF9BQBvgcjr17+8/pN/M6KF3t/1FoYMmUy1P8uona5/RQxU7NWri7+wrEkCbYpgHpyzEtcLCQnXv3l1S3ZmIHH0BIF68Med5PbDxj/Vth2nqs7P+q6wuR9lYFdB2PLfwI/116WTVOq2bPqUHQ/p12lh97/vPSDFYigMgfkQjG8TkKAsAQNu2apP1fLYefhEMgVZ03ciz9MQpLyoj4LH0lzsd+m3VF/rriz+Q+Hs/gCNEOAQAHLHCqrWWdjd1sKkSoO06o98xeu577yjPn2HpDxiGntEq3ffPCQoFDr0sBwAOhXAIADgyviptcVVauvIzh9tTC9DGDezYVdOu+EgDA10jrr3lKdYd/zpdAV+1DZUBaAsIhwCAI7J95QxtcVv3Nztm4PdsqgZo+7JT0vTqD9/XWA2NuPZx0l797PlTVVW114bKACQ6wiEA4Ih8/d10S9tlSmccdZJN1QDtg8vp1F+vfUWXpYyXI2yt4VdJ1frpS6dr9+7tNlUHIFERDgEAR2Tj7iWWdkd/mlK9XpuqAdqX33z/T7ox9wdyhQXEb5P8uun1s7StZHMjTwJAJMIhACSwXRW1+vf8zfpkZbFCIRt2Kqwo0Uaj3NLVwTsg9nUA7djPzrtft3e7UUlh3wNWeEP60VuXaP0uRhABNA/hEAAS1J4qny565iv9ZvpyXf/CQj01a13si9jylZZ7rVvr98kbG/s6gHbu6gm36Df9JistaD0HcZunRpe8dYUWbdtoU2UAEgnhEAAS1CPvr9TmXVX17b/OXqedFbUxraFgzSfa7rJuRnNan5ExrQFAnQvGXqOHh/5OuYGgpT/gKtG1H1yjORu/s6kyAImCcAgACWju+l2atrDQ0lcbCOmlubFdX/Td1nmWtivk0Cm9hsS0BgAHnDHqYv0j2EXd/AFLv+nao599+mO9s3KBTZUBSASEQwBIMDX+oH4zfVmD116at1nVvmCD11q/kDKt9xdbujJDefKEjSQCiK3+x16hF4qK1dfns15wVuieeT/Ty4s/tacwAHGPcAgACeavs9drw87KBq+VVvr0328LG7zW6goX6Duv29LVMXVYbD4bQOMGXaDOpqGpRTs0pDZsqrmjRo8tul2fb1xhT20A4hrhEAASyLod5Xpm9qE3nvnX5xsUjMHOpaFNX+m7sM1ohuUNj/rnAmhCaq7U53RlhUL6Z9EOHV9dY7lsOAJ69KtnbCoOQDwjHAJAggiFTN395jL5gweCn9Nh6N6JR1vu27SrSjNXRH/r+g0bP1Op02npG9f7uKh/LoBmGHqJJCnVNPXX4h0aW2UNiMW1G+yoCkCcIxwCQIJ4bWGBvt6029L347G9df3JvTW4a4al/+9zovyLX6BWn1daRzBdAa/GdOeMQyAuHDVRciVJkrymdFVZmeVyrVGsUCjU0JMA2jHCIQAkgB3lNXr0/ZWWvu45ybp13AAZhqEbT+ljufbtlj36ZnNp9Ara+q1mJXstXZ7aoXI4+LECxAVvujTgrPpmT7/fctlw1GrtrujPMACQWPgpDgAJ4HfvrFBZjXVr+ocvGKpkT920znOHdlHXzCTL9Wc/i97oYcmGj7U4yRoOM4zRUfs8AIdhyCX1X+YFgnKb1rXIC7etiXVFAOIc4RAA4tynq3fo3aVFlr7zh3fVKQM61rfdToeuO7m35Z6ZK4u1oaQiKjXNKphtaXuCDuUns1MpEFf6nyl566acOyV1Dzv78Lvi9TYUBSCeEQ4BII5V+QK6d/pyS19mslv3TRoUce9lx/dQetKBMwZNU/rXFxtbv6hQUB/XbLXWVNlVndPTWv+zABw+d5J01KT6ZvjU0g17N8W4IADxjnAIAHHsz5+s1dY91fXtwcZGTc//jzp89Xup8Ju6BLhPmtelK0b3sDz/328KtbMi7JyzI7S3cL4WeqwH3e8pO04d0ryNPAHANkMvrv+yZ9jI4faqGJ2JCiBhEA4BIE4FQ6b+M39LfTtHZXot6VH1KXhT+uov0j/PkP5vtPT5H6WybZKkH53YW26nUf9MbSCkl+ZubtW6Pls5TQHjwGd4QqZKK0aoQ5rnEE8BsEXv06SUDpKkHgHryOHeQFHk/QDaNcIhAMSpXZW1lk1oJjrnKc0MW0O4c7X0yYPSHwdJL16gvM3/08VDcy23vDRvs6p9wVar6+MdCy3tXlUpkulVh3RGDoG443RJgy+QJPUKGzn0O3YoEGy97w0AEh/hEADiVPFe63TQs5wLG7lTkkxpw6fSmzfo4Q0X6wLHF/VXSit9+u+3rTN9rMpXqa+Ceyx93op+ksS0UiBe7du1tEdYODQcfq0oKbCjIgBxytX0LQAAOxSX1UiSHN5tSvVsVYV7o2YquRlPBjQx63ltCpRquVm3g+lfF6xRp86D5XAYTTx7aKu3zlPtQVNKnaapwrITJREOgbjVfbTkSVMnX4WSQyFVH3Qe6Tdb12pYXi/7agMQVwiHABCnistr5OnwsbwdP5Yk3ancJp4I95WS9ZUkqULSr+a0bn2SdEx1UJ8Fe0mSOhIOgfjkcEjJOTJ8FerhD2i198D64O9K1kuaYF9tAOIK00oBIE4V7amUJzcKia4V5VfkSDLkcTqUkczfG4G4lZwpSeoRsE4t3VTWuhtWAUhshEMAiFNb9m6X4fDZXUaj0oMhBcqHSpI6pHlkGEc2ZRVAFCVlSYo867C4muMsABzAn3kBIE5tqyyy/AnPYZoHNpTI6SM5nJEPmaa0d4sUsIbKXcrQHjNNDkPqnp0il/MwglzQL+3eJEnqEgzo+j1lesA/TJLYqRSId8lZkiLPOiznOAsAByEcAkCcKqkullIPtHv4A3pna5HU4wTp4vcbf3DXeunvp0u1e+u7AmaxrvbfrbmhwepyVCf969qRLR/pm/tXafnd9c1iM0srzR6S2IwGiHuNjBwGHDvlCwTkcfErIQCmlQJA3NrjK7G0O+8/j+yoiYd+MLevdPE/LF0uI6Sn3E+pi3Zp1qodmrmiuOUFrZ1hac4ODpdUFzA7pHki7wcQP/aNHEYeZxHUku2bYl8PgLhEOASAOFQbCKrG3GXp67J/I4mB5zb9BgPOkk6729LVwSjTM54/ySufHnxnhap8gUYeboCvUtr8paXr09DwA+/NyCEQ35KzJUk5oZDSgyHLpW+3rbGjIgBxiHAIAHGopLxWhnuPpS8vEJQ6DaobGWyOUyZLA862dA13bNADrhe0dU+1np61rvkFbZwjBQ+sYwzIqS9DQ+rbhEMgzu2bVmpI6hGwTi1dsXND7OsBEJcIhwAQh4rLauQIC4ddAoGmp5QezOGQLnxWyu5t6b7c9akuc87SPz7foHU7Kpr3XmtnWprLHUerXCn1bTakAeLcvmmlUuTU0m0V22JcDIB4RTgEgDhUXFYrl2u3pS8vGGxZOJTqfiG87BXJnWLpftA1VYNDa3X/28tlmuah38M0I8Lhp6FhljZrDoE4t2/kUJLSQtZppb5gbYyLARCvCIcAEIcK9+yR6aqx9OV5c6Uuw1v+Zp0HSec/benyGgE94/l/WrN+g95Z2sRW9jvX1B2PcZAPa63hsBMjh0B8O2jk0BP296CAaZ1mCqD9IhwCQBzasGdrRF9e/7Okwz1ofsjF0gk/t3R1MUr1tOcvevSdZSqvOcQvh2G7lAbTumi12d3Sx5pDIM4dNHLoDpstEAi1YHMqAG0a4RAA4tDePSst7cxgUClHn39kbzr+QanXWEvXGMdKXV/zvP40c23jz4VNKd3T7TTtP8JCktxOQ5nJ7iOrDUB07dutVJLcCguHjBwC2IdwCABxKLlsvqXdOWhKPU86sjd1uqRLnpeZ0c3S/WPXByqd/2+t2FYW+UxtubT5K0tXYYeTLe3cVK+Mwx3RBBAbSZn1X4aPHAZDhEMAdQiHABCHUoKrLe1sV47kbIXRubSOMn7wkkyHdQOZR51/1z//+45CobDFSBvnSAf/4uhwa23qcZZbOqSzGQ0Q9xxOyZshSfKETytl5BDAPoRDAIg31XtkOnZZurIz+rXe++cfJ2PSk5auZMOnW3Y+qLfnfWe9N2y9oXqeoO01LksX6w2BBLFv3aE77G9AIZM1hwDqEA4BIM7UrPxQO1zWb895eSNa90NGXKPAsddauno5itVhxi+0p3LfLqmmKa392PpcvwnaWeGzdBEOgQSRXDe1NHzkMChGDgHUIRwCQJzxr/xA253W0bk+Hfu0+ue4Jv5BZbnWIynG6lstfOGuusaOlVJZofWh/meqpMJ6JhrhEEgQ9SOHYeGQkUMA+xAOASDOGCUrtN3ltPT1zOzWyN1HwOVVxjWvqtyZZekev2Oq5n3wsrTOukupMrtLHQdqZ7k1HHbkjEMgMew769ATtltpSIRDAHUIhwAQT0xTVZVbVeOwfnvuktolOp+X2U21Fz6ngGn9vMHzfqXahS9b7+0/QTIM7YwYOWRDGiAhNLLm0GTkEMA+hEMAiCdVpdplWNf0yTTUMaVj1D6yw5BxWjb4TktfulEt7+411hv7nylJEWsOOzKtFEgM+0YOw6eVMnIIYD/CIQDEkz2bVOSyrjdMMrLkcrgaeaB1HPv9u7U4a3zjNzg9Uu9TVBsIam+1dfOKDkwrBRJDI2sOTcIhgH0IhwAQT3Zv1nandb1hmjt6o4b1DENH3/i8Njl7NXy950mSJ1W7wkYNJTakARLG/jWH4eHQIBwCqEM4BIB4smeztoeNHOZ6O8fko70pGUq++lWVKTXi2vqsEyQpYr2h02EoK9kdk/oAHKFG1hyKkUMA+xAOASCe7N4csVNpXmpswqEkde51tApP/7NCpmHpv21RZxWUVkWEw9xUjxwO670A4tT+NYcKHzkM2lAMgHhEOASAOGLu2Ryx5rB7RhSOsTiEQad+X18MuEt+sy6k/i0wSUurO+rmf3+rrburLfcypRRIIEnZkiKnlYpppQD2ie4OBwCAFgmVblJRmnXksG92bMOhJI294tf61Quj9dWqQhUpV5K0bOtebSmtstzHGYdAAmlkt1LDCCkQDMoVtt4ZQPvDyCEAxItQSMG9hSoJ+wVtQG6PmJdiGIYeuGyskjtaPztip1JGDoHEsW/NoSdizaFU6a+N7ATQ7hAOASBelBdplxFUyLCu4eue0dWWctKT3Hr2quOU4ml8NKFDuieGFQE4IkmZkiJHDiWpmnAIQIRDAIgfe7ZEbEZjmG5lebPsqUdS/87p+sMlxzR6vSMjh0DicLokT3qD4bDSRzgEQDgEgPjRwGY0XuXIMOzdDXTisC66YWzvBq8xrRRIMMlZEbuVSlK1P/IMUwDtD+EQAOJFA8dYpLk62FSM1V1nH6UxfXIi+gmHQIJJymp4zSEjhwBEOASA+NHAyGG2J3ZnHB6Ky+nQU5ePUF5GUn1fktuhofmZNlYFoMWSsxpecxggHAIgHAJA/Ni9WdvDdirtnBof4VCqO7bilRtGa2z/DhrSLUNPXT5Cmcluu8sC0BJJmXJKcoYFxCo2pAEgzjkEgPixZ7O2p1q/Leen27NTaWP6dEzTS9ePtrsMAIfroLMOgwetZ64JsOYQACOHABAfgn6ZZVsj1hz2zupmU0EA2qR9Zx26w2aWMq0UgEQ4BID4sLdANTK1J2xa6VEdejTyAAAchv0jh2E7ltayWykAEQ4BID40sFOpJA3IzbehGABt1r6RQ0/YmkNGDgFIhEMAiA97NqvIGbYMPJSiNG+qPfUAaJuSsyUpYsfS2oDfjmoAxBnCIQDEg92bVRw2cugxI88VBIAjsm9aafjIYU2QkUMAhEMAiA8NnHGY6uxgUzEA2qyk/SOH1m5fkJFDAIRDAIgPuzerKGzkMNvTyaZiALRZjY0csuYQgAiHABAf9mxWQdjIYefULjYVA6DN2rchjStst1JfMGBDMQDiDeEQAOzmq5IqS1TgtobDftk9bSoIQJuVlCkpcuSwljWHAEQ4BAD77dmiasPQjrCRw8Ed+9pUEIA2y+mSPOkRaw79IdYcAiAcAoD99mxWYVgwlKTjuvazoRgAbV5ydsTIoZ8NaQCIcAgA9tu9WVvCppQqkKG8jAx76gHQtuX2jTjn0Bfy2VQMgHhCOAQAuzWwGY1X7FQKIEq6DIscOWRaKQARDgHAfrs3RYwcZrrYqRRAlOQNkzusi2mlACQpcpELACSIlUVleujdFaryBXXKgI76/nH56p6TYndZLbdnc8ROpZ1TutlUDIA2r8sxEdNKjWCFTcUAiCeEQwAJyTRN3T5tiVYWlUmSFhfs0V8+WasT+uTq+yPzdc6QLkr2OJt4lzixe4sKOqZaunpn9rCpGABtXk4fOWX9/uj277KpGADxhHAIICEVl9XWB8ODzd2wS3M37NL9b3+n847pokuO664RPbJkGIYNVTZD9W75aveqyGXdfGZgh942FQSgzXM45XdlSzpwtqEnsMe2cgDED8IhgITUUDA8WEVtQP9ZUKD/LChQ346p+v7I7rro2G7qlJEUowqbqXiFtrpdCoWF1+F5HGMBIHp87lxJ2+rb7uBe+4oBEDfYkAZAQlrRRDg82PqSSj32wSqd8NgsXTf1a32wrEi+QCiK1bXAlq8idioNBVI1oGNHmwoC0B74vdYdkd0maw4BMHIIIEGFjxz+YGS+ThnQUdMWFurztSUK22tBkhQMmZq1aodmrdqhTuleXTWmpy4/voc6pntjVHUDNs/VFrd130BXqIOS3AmyXhJAQvIndZGqFte3DbNGqq2QvGn2FQXAdoRDAAkpfORwePdsTRrWVZOGddW2PdV689tC/febQm3aVdXg8zvKa/XHmWv09Kx1mnRMF/3oxN4amp8Zi9IPCAWlggUqSLN+K05z5MW2DgDtTiApTzro22PAMKTi5VKPMfYVBcB2TCsFkHCqfUFt2llp6Tu6S3r9112zkvXzM/rr01+dpmk/OUHfPy5fKY3sXOoLhvTmt1t13tNf6OJnvtI7S7bJH4zRlNPtyyRfecQZhx2Susbm8wG0W2639dgfn6G670kA2jVGDgEknNXF5QqZkgyfXBlL5ErepncKF+uDon2butRWSoVfS3s2S0G/sh1OXdXFqYDpkC9kyBcyFJRDQTkUMJ3aqUwVmh30XU2mfjXL0P1fODU6f6DuOfVK9ciK4tq/LXMlKeKMw/y07tH7TACQ5HFap7P7DUMqWmJTNQDiBeEQQMJZsa1uSmlS3ltyZ30rSXp97dzIG1M9kjyNvIspKbjvVSKpRPt/VQpI+nLPFzp3+ss6KnWCHjjlpxqSF4VzBzd/pYCkrWEb0vTP6dX6nwUAB0lyWdda+2RI25faVA2AeMG0UgAJZ2VRmQxneX0wjBbDUavV1e/q8g/P0zn//oU+37ii9d7cNKUtc1Xkctat9TnI0M59W+9zAKABDY4c7lgpBf02VQQgHhAOASSclUVlcqauj90HGiEVBmbr5s8u1fgXrtHbK+Yf+XvuWi9VlqggbKdSM5ikozuxIQ2A6IoYOTQMKeiTSlbbVBGAeMC0UgAJJRQytWp7uVJyVujg0yp6+fw6vqbmoB5D6jhQ6jBAMkOSGZRC+/8bOPB1bZm0d6tUXVr/5C6nU5+mJEccTC9DKtYi3fv1j/WXud107sAf6xdjLpDHdRjfSrd8JUkRZxya/lzlZSS3/P0AoAW8LuuUe//+b3fbl0p5Q2JfEIC4QDgEkFAKdlfpTP8nWpy2SCU6sAPpZeXlurJs3yHOPU6Uznlc6jKs+W+8t1Ba97G0dqa0YbYKSnfrhcwMTU9Lk89hRNy+w7VVU9c/qDdWP6YRWefqzjN+rp7ZnRp440ZsrlsjGb5TabI6ydHA5wFAa0p2hofDfd93ipZKw6+woSIA8YBwCCChFC+ZqZ+n/kvnu6zHPZxQXSNl5EtnPiQNvlAKH/VrSma+dNwP614Bn7pvmat7187Qjcvf0CuuKr2Wka4KR+RM/HJXrT6rmK6v3pquozRAV4+5Q+ccfWLTn7dv5HBL2MhhlodjLABEX5K7kXDIpjRAu8aaQwAJxbn2Q81NTrL0dQ4E1fuEO6Sffy0NuajlwTCcyyP1OVU662F1uu073Xruv/Se9xj9srRMuYFgg4/4HdIyxxpNXvATTfzHCXpyxlOq9tc2/P5lRdLuTZIij7HoktrtyGoHgGZIbmi3UqnurEPTbOAJAO0B4RBAQkndvUJzk61r8k7oc7aMM+6RPCmNPHUEHE6p/wTlXPGKfnzjN3p3wI36TblLg2sbCX6StngqNLXo75rw4ijd/sr1WrtjS9gNdaOGIUkFLuuGNH0ye7b2vwAAIkSOHNYd8KPasvo/XgFof5hWCiBxmKY61azT152zLd0n9Bwfm89P7aC0sbfqspN/qUu3fqvPP/uT3t2zQDNTPRHHUUjSXpepmf4F+vT9c9U3mKPM7C5KyciVSlZJnTooYBgR6xmP7tAnNv8WAO1aStjIoWkYCkh1571uXyrl9LajLAA2IxwCSBhlxRu00RtQVdjav9FdRse2EMOQkX+cTrnyZZ3iq9LGBS/q9WXP6SNPuXY0sHNpwDC02rVbKt8tle/rTI0c5TRDbg3unB/l4gFASg4bOZTq1h26TbNuU5pB59tQFQC7Ma0UQMIoWrUgYr3hwOyByk3OtakiSZ4U9T75Jk3+6QL975xp+pUxSMfUHN4h0iFfB/XITW3lAgEgUqonKaKPTWkAEA4BJIyqLd9GhMMTuzZjZ9AYSe06VNde85pevm6R/tL5Ck2ocssbCjX7eWfFCcpMdjd9IwAcoWRXQyOH+77Yviy2xQCIG0wrBZAw/DuXaXmO9ReaMV3G2FTNIbiTdfrZd+t03a2Nq+fo7c//nyor1quDuVsOWXcB/M7sqfeDJyhYna9hHUbaVDCA9qbBkcP9O5aWF0kVJVJaxxhXBcBuhEMACaMwuEEh48AmCi45NaLzCBsralrvgafo1oGnyBcIaeaSDZr3xcdK27FIAxwFKjJz9Unge/IpRYYhXXcSG0AAiI0Utzeiz3fwxlrbl0j9YrTZF4C4QTgEkBAC5Tv1XZJP0oFfaAam9leSK/Kv3/HI43Jo4nH9NPG4flq9vVwri8qUZ5p6UJLDMDSkW4b6dUq3u0wA7USyO3IKu//gcFi0lHAItEOEQwAJoWjNwoj1hif3OsOmao7MwLx0DcwjCAKwj8PhkBlyynAE6/t8B5+sw7pDoF1iQxoACWHt+s9UEPaX7jN6n2pTNQCQ+AzTOrV068FH8bBjKdAuEQ4BJISlpd9Y2mkhp47KOcqmagAg8aUbvSxty+yMXeul2orYFgTAdoRDAAlhtVlkafd3dJXD4FsYAByuobmjLO25yckHtUypeHlsCwJgO36zAhD//DXa4A5YugZnj2rkZgBAc5w/8HRLu9DtUoHLeaCDdYdAu0M4BBD/SlYqYFi70rKH2lMLALQRZ/YbLgXTLH2W0cOiJbEtCIDt2K0UQIsFQ6Y+WF6k7XtrlJeZpPzsFOVnJys31SPDMJp+g5YqitwYoVqe1v8cAGhHXE6nOruHqjg0t75vbnKSflC+b60hm9IA7Q7hEECL3f/2cv17/paI/mS3U/nZyfteKeqeU/ffAZ3T1Ldj2uEHxwamNvkCocN7LwBAvTFdTtDbWw+Ew/lJSQpKckrSjpVS0C85I89EBNA2EQ4BtIhpmpq+aGuD16r9Qa3dUaG1OyJ3uDu+d47umDBAo/vktvxDCYcAEBWXDRmnt7f+sb5d7nToO69Hw2p9UtAnlayW8obYWCGAWGLNIYAWCZlSlS/Y9I1hFmws1aV/n6er/jlf327Z3YIPDDW4Y14t4RAAjtiQvB5yBfIsfV8dfKQFU0uBdoVwCKBFAqHIUOZowWzRL9bt1EV//Uo/en6BlhXubfqB3RslX+RIJCOHANA6eqUea2lbzjtsYM03gLaLaaUAWiQYMiP6Zt1xmtwuhwpLq1Swu1qFu6tUuLtaBaVVWrejQrsqfRHPfLq6RJ+uLtGZgzrr9jMH6Ki8jIY/sJG/WvuChEMAaA3jeo3VutUf1LeXer2qNAylmiYjh0A7QzgE0CKBBsKhx+VQ16xkdctK1uiwa7WBoF77ukBPz1qnHeW1Ec/OWFGsmSuLNXFoF906foD6dbJuq97YX619fsIhALSGy4edpr+tcsow6pYMBAxDXycl6bTq6ro136YpRWMnagBxh2mlAFokGIwMh66D55X6a6SSNVLpBmlvobw1u3TN8CzNuW207junvzqkRu56Z5rSu0uLdOafPtPtry3W5l2VBy5uW9RgHbWMHAJAq8hNSVeq2dfSVz+1tLZM2r0p9kUBsAUjhwBaJGiaMlx75e04Qw5PiSTpls9ekctpSNV7pF3rJLPx4HZUJ8mUoZAMBeVQqZmubWaH+usflkofvi51TPMqPztZRu0aqUtnlTqdlvepDbR8UxwAQMOGZI/Sgr1r6ttzwzelyeltQ1UAYo1wCKBFgiFTSd3+I1fKpvq+70oPOvPQ29LzsKrkVOSZiaVBqXSnJI9T+07csmBDGgBoPRP7n6oFC/9d397ocWu706m8YLBuev+g822sDkCsMK0UQIv4gyE5kzfbXYYC/qSmbwIANMukgaOkYLKlr370sIGzZgG0TYRDAC1SG/DLMCLXHcZSsLq75Otmaw0A0JZ4XC51cA229NWHw22L6haHA2jzmFYKoEVqApE7jv6ywq+smrIDHZ506dS7JKdHCgWlUEAyAwe+DgWkPVukZdPC3smhBR0v0vRt2ZKk653vqZ9jW/3VT4PD9aHvdAUr+6m2czT+dQDQfo3qfII+2L6wvj0vOUkhSY7KHdLONVLHgfYVByAmCIcAWsQX9Ef0nVe6Q52DB20Qc8nT0pCLmn6zjIHSh7+2dF1cPU2bau7WgkBf/dT7d2UaVfXXZvqOVzA0SJJU7WdDGgBoTT8YfIY+2P5UfXu306lVHrcG+fzShtmEQ6AdYFopgBapDQQi+lwHTzcacI40+MLmvdmYn0on3WrpMgLVmuqdojMciyzBUJKWhvrUf13tIxwCQGsamd9PjkBHS1/91NINn9lQEYBYIxwCaJHaoC+iz6194dCTLk18smWHJY9/QDrmCktXSrBMf3P/ydK3x8hUkXLq24wcAkDr65ky3NKem7xvk5pNX0jByD8OAmhbCIcAWqShaaWu/QOH438rZbZwoxjDkL73F6n/mZZuj2ENf4sCvSQdCJ01hEMAaHWndj/J0l7k9araMKTavVLREpuqAhArhEMALeJr4C/HbtOUuo+RRl5/eG/qdEvfnyp1G9noLUvNPpa2P2jKH+SsQwBoTZcPO12meeDXQ5/D0LdJ3rrGxtn2FAUgZgiHAFqktoHdSl1OT93on+MIvqV4UqUrpkm5/Rq8vDzUO6KP0UMAaF1dM3KUHOpl6WPdIdB+EA4BtIhRvtnSdpmmjFPubJ1d7FJzpavelNLyIi4dvBnNfqw7BIDWd1SmdRbH3KR94bBgvuSvsaEiALFCOATQImb1LkvbaSpix9Ejkt1TuuoNyZtR3/VtqJ+KlR1xa42PaaUA0NrO7XeKpb3G69FOp0MK1NQFRABtFuEQQIv4QtbdSt2mKbk8rfsheUOkH38sDbtUlYOv0K3BX+rgzWj2Y+QQAFrf+YPGyAx5LX31o4cbmVoKtGWEQwAtEgzbrdQZrQ/qOFC66O9K/f4zOum44Q3eUuVjW3UAaG0pbq9yHIMsffNYdwi0C4RDAC3iD1nDodts5MZWdPNp/eRyMHIIALEyotNoS3tuclLdibbbvpVq9tpSE4DoIxwCaBF/rEYOD9I9J0WXHJcf0c9upQAQHRcfdbqlXeJyaZ3bLZkhadOXNlUFINoIhwBaJHzk0BmDkUNJ+tnpkaOHZow+GwDam5N6HiUjkGPpO3CkxezYFwQgJgiHAFokfM1hLKaVSnWjh9ec0Ku+nZ7k0jHds2Lz4QDQzjgcDuUnDbP01YdDNqUB2iyX3QUASCwRI4cN7CIaLfece5R65qZoS2mVLh6Rrw5p3qYfAgAclpPzT9R/Ns2uby9M8sonyVOySirfLqVHnkkLILExcgigRYIh6w6hsZpWKkkup0PXnthL900apEFdM5p+AABw2K4YOk6meeAPgDUOhxYn7fuj3MY5NlUFIJoIhwBaJGBaRw5dMRw5BADETq+cTvKGelj65nKkBdCmEQ4BtEgwZN0h1MGmMADQZg1IH2Fpf3XwukN2BQPaHMIhgBYxFbK0DUYOAaDNOrPPWEt7pcejPQ6HtLdAKt1gU1UAooVwCKBlzFDT9wAA2oRLhpwsM+Sub5uGoXnsWgq0WYRDAC1iRoRDRg4BoK1K9yYr0zjK0se6Q6DtIhwCaJmINSaEQwBoy47JHWVpz01OkinV7VgaYjYJ0JYQDgG0UPiaQwBAW3bhUadb2kUulza7XFJ1qVS83KaqAEQD4RBAy4RPKzWIhwDQlo3rO0wKWs+Wncu6Q6BNIhwCaKGwcGgSDgGgLXM4HOriGWbpqz/SYsPs2BcEIGoIhwBaJmLk0J4yAACxc2LXEyztr5OT5JekzV9JAZ8tNQFofYRDAC3EyCEAtDeXDT3D0q50OLQ4ySv5q6StC22qCkBrIxwCaBEzbLdSg6FDAGjzjuqYL3cg39I3KyW57guOtADaDMIhgBZiQxoAaI+GZJ9kaX+SmrLvSAvCIdBWEA4BtIgRNnIYfuohAKBtunLoREu7yOXSCo9HKvxaqq2wqSoArSmm4TAUCikQCLTomWAw2OJnAESPGb4hDdNKAaBdmND3GDkCHS19H6cmS6GAtGWuTVUBaE1RD4fz58/XBRdcoNzcXDmdTrndbvXr10+PPvqoamtrG3zmo48+0oQJE5SZmSmXyyWv16uhQ4fqmWeeiVjvdLg2bdokwzCa/XrggQcafa+VK1cqJydHP/zhD1ulNiC+seYQANojh8OhozJOtPR9nLJvailHWgBtQlTD4QsvvKCTTjpJb7/9tqqrqzVmzBj17dtX69ev1z333KMJEybI57Nuf/z73/9eZ599tj7++GNJ0oknnqiuXbtq+fLluvnmm3X55Ze3Sm2GYcjr9Tb5Mvatp3I4Gv6fqri4WOeee652797dKnUBcY+RQwBot34w6FxLe5PHrQ1uF+sOgTYiauFwyZIluvHGGxUMBnXHHXdox44dmjt3rtatW6dnnnlGhmHo888/15QpU+qf+eCDD3TffffJ6XTq8ccfV0lJib788ktt2rRJ9957ryTptdde0yuvvHLE9fXs2VM1NTWHfK1Zs0ZJSUlyu9267LLLIt5j06ZNOvPMM7Vp06YjrgdIHOHhEADQXpx/1GgZwSxL38epKdL2ZVLlLnuKAtBqohYOJ0+eLJ/Pp8mTJ2vKlClKS0urv3bTTTfpoosukiS9/PLLkuq2x7/99tslSU899ZQmT54sj8cjSXI6nXrooYc0cuRIyzPRdtddd6m6ulq33HKLBgwYYLk2c+ZMjRw5UkuXLlX37t1jUg8QFyKmdjNyCADthcvpVJ+UMZa+T1JS6r7YNMeGigC0pqiEw8rKSqWkpGjUqFG6//77G7xn1KhRkqStW7dKkgoKCtSnTx9NmDBBN910U4PP7A+H+5+Jpnnz5unVV19Vp06dIv4N5eXlOuecc1RWVqa///3vuu6666JeDxAvzIiRQzY9BoD25IL+Z1naK70eFbqcnHcItAFR+a0uNTVV06dP14IFC5SamtrgPdu2bZMkpaenS5J69Oih9957TzNmzKhf59fUM9FimqZuvfVWSdIjjzyijIwMy/VgMKhjjz1WCxcu1A033BDVWoC400qbQgEAEtNlw06Rgtbf7z5JSZHWz+JnBJDgXHZ8aDAY1FtvvSVJGj9+fLOeKSsr08yZM1v0zOF69913NX/+fPXt27fBHUgzMjI0b948OZ3OVv3cwsLCQ14vKipq1c8DDg8b0gBAe5bk9ijfO0qFgdn1fZ+kJuvaos11AbHfOPuKA3BEbAmHzz//vLZs2SLDMOpH6JryxBNPqLq6WsnJyY1OO20tjz76qCTpzjvvbDAANrZz6ZFi7SISQvhfhRsZ6QcAtF0T+56pZ1fPrm8v9npV4nSo47xnCIdAAov5YqGioiLdddddkqTrrrtOw4cPb/KZZcuW6Q9/+IMk6Z577lFeXl7U6ps9e7bmzp2rvLw8zi0EGsTIIQC0d9ceO15myFvfNg1Ds1JSpHUzpZI1NlYG4EjEdOQwFArpqquuUmlpqXr37q0nn3yyyWeqqqp02WWXyefzafTo0fXBMlr2jxredttt8nq9TdzdugoKCg55vaioSMcff3yMqgEaYx05NAiHANDupHuT1dl1rHaE5tX3fZyarEvLK6T5z0iT/mRjdQAOV0zD4X333adZs2YpKSlJ06ZNU2ZmZpPP3HDDDVqxYoVyc3P16quvyu12R62+tWvXasaMGXK73br++uuj9jmNyc/Pj/lnAi3HURYAAGlCz/H698YD4fDrpCTtdTiUufg/0hn3SSk5NlYH4HDEbFrptGnT9Mgjj0iSnn322fpjKQ7liSee0CuvvCKXy6Vp06apV69eUa3xueeekyRNnDhRubm5Uf0sIFGZJtNKAQDSDSPPlRk6MM4QNAzNTkmWAtXSty/YWBmAwxWTcDhv3rz69Xt33323rrnmmiafmT59uu6++25J0tNPP60zzjgjmiUqGAzqxRdflCRdffXVUf0sILExcggAkHJT0pXjGGbp+zglue6LBf+Qgn4bqgJwJKIeDlesWKHzzjtP1dXVuvTSS/Xwww83+cxnn32mK6+8UqFQSJMnT9ZPfvKTaJepGTNmaNu2bcrKytLEiROj/nlAwgofOWS3UgBot07Pt/7x/qvkZFUZhlS2VVrxtk1VAThcUQ2H3333ncaNG6edO3fqtNNO0wsvvNDoAff7zZ49W5MmTVJ1dbWuuOIKPfbYY9Essd7rr78uSTr77LNjvhENkFgYOQQA1PnxcZNkmgd+nfQ5DH2enFTXmPeMTVUBOFxRC4fFxcUaN26ctm/frqFDh+qtt95qMnQtX75ckyZNUkVFhcaPH6+pU6c2GSZbg2ma+vDDDyVJp59+etQ/D0hs4eEw5ifiAADiRPesXGWYR1v6PklNqfti60Kp4GsbqgJwuKL2W92UKVNUXFwsSVq5cqU6d+6spKSkBl9z5syRJD344IOqrKyUVDe1ND09vdFnNm/eXP9Zmzdvru9/6aWXWlzrokWLVFRUJIlwCDTFDAuH4VERANC+nNjlNEv7s5Rk1e7/2/68v8a8HgCHL2rhcPHixfVfBwIB1dbWNvoKhUIRz/j9/kM+Y5oHfiU1TbO+PxgMtrjWDz74QJLUrVs39e/f//D+wUA7EQxbc+g0nDZVAgCIB3VTSw/M9KpyODQ/ad/U0hVvS3sOfY4zgPhhmAenLMS1wsJCde/eXZJUUFDAuYiwxZ1/O14fJlfXt0cHe+mf171jY0UAALsd/9yFqnauq29fUF6hh3aW1jVO+qU04Xc2VQa0XdHIBiwWAtAiPllHDr2G26ZKAADxYmTHUyztT1OSFdjf+Gaq5KuMdUkADgPhEECL1ISHQwfhEADau+uOPc/S3ut06pukfRsR1uyVFr9iQ1UAWopwCKBF/AbhEABgNTK/nzzBHpa+j/fvWipJ8/8mhcLOyQUQdwiHAFrEF7Y/KdNKAQCSNDxnrKU9KyX5wFyTXeukdR/HvCYALUM4BNAitUZYOHQe+vxSAED7cPWwSZb2DpdLy7yeAx0cawHEPcIhgBbxhYXDZKenkTsBAO3JaX2GyBnIs/R9knLQ1NINn0rFK2JcFYCWIBwCaBGfYW0nM3IIANhnUOZJlvaM1FTrYoT5z8S0HgAtQzgE0CLh00qTXYwcAgDqXD74XEt7q9upNe6D1qYveU2q3BnjqgA0F+EQQItEjBy6kuwpBAAQdyYOHCkjkGPpm5mWdqARrJW+eT7GVQFoLsIhgBYJD4cphEMAwD4Oh0P9006w9L2dmmu9acE/pYAvhlUBaC7CIYBmC4VCqg0Phx7CIQDggIuOOtvS3u726Yvkg35WVGyXvpse46oANAfhEECzVfv9ChnWdJjqJhwCAA74/uCTpWCGpe/uDp2113HQz495/yeZpgDEF8IhgGbbW1sZ0ZfuSWngTgBAe+VxuXR2t2ssfXtcph7LPWgtYtESae3MGFcGoCmEQwDNtremKqIvNSndhkoAAPHs8Qk3KD002NL3blqqPk5JPtDx6cOMHgJxhnAIoNnKaqoj+jKS0hq4EwDQnjkcDv3tnMekoHXpwe865GiXY9+vn0WLpdXvx744AI0iHAJotnJf5MhhGiOHAIAGDMvrpYt6/tzSt9vp1O865Kh+vPDTR6RQKOa1AWgY4RBAs1X4rCOHnpAplye5kbsBAO3db0+/WjkaYemblZqid9P2rVcvXi6t/J8NlQFoCOEQQLOVV1dY2l7TlJwem6oBAMQ7h8Ohf058TAqmWvofzcnRdqezrjH7USkUtKE6AOEIhwCarcpXbmknmyHJ6bapGgBAIujfoYuu6X+Hpa/c6dBv908vLVklLX/TltoAWBEOATRbVW0DI4cur03VAAASxZ1jv688x0mWvq9SkvV6+r5NzT57TAoGbKgMwMEIhwCardrHtFIAwOF57ryHZQQzLX1TcrJU4HJJu9ZJy6bZVBmA/QiHAJqtxm/drTQ5ZDKtFADQLN2zcnXT4F9b+qodDt3bMUdBSfrscSnot6U2AHUIhwCarTYsHNaNHDKtFADQPDePnqSe7nGWvm+TkvRyRrq0e5O0+BV7CgMgiXAIoAVqAg2FQ0YOAQDN96/vPShHINfS95fsLK1zu6U5f5ACtTZVBoBwCKDZfAHrOYdJpik5nDZVAwBIRJ3TMvWrEffLNI36Pp/D0G865si/t0Ba9JKN1QHtG+EQQLP5wv6a6+BbCADgMFx97BkamHyupW+F16t/ZmVIc56U/DU2VQa0b/xmB6DZ/AHrIcUG30IAAIfpX9+7T85AZ0vf37My9V1tifTN8zZVBbRv/GYHoNkCAesucqZhNHInAACHlpWcqvvHPCTTPPDraMAwdH+HXAU+/6PkqzrE0wCigXAIoNmC4QcUG3wLAQAcvosGn6Dh6RdZ+tZ4PXrHqJS+/qdNVQHtF7/ZAWi2YCA8HLIZDQDgyDw76ddyBbpY+p7OzlTll/9Pqi23pyignSIcAmi28JFDg2mlAIAjlOr16oYht1j6drhcesnjlxb83aaqgPaJcAigWUzTVCh8WinHWAAAWsFNo85VemiQpe+5zAztmPuUVLPXpqqA9odwCKBZqv1BGWbI0mew5hAA0AocDofuPXGyZB7oq3Y49LdkSfOesa0uoL3hNzsAzbKnyi+HwsIhI4cAgFZy7sDj1M11iqXvjfQ0rVnwV6l6t01VAe0L4RBAs9SFQ+s5hw4H30IAAK1nyvi7ZIRc9e2QYejP6R7pq6dtrApoP/jNDkCz7Kn2yXHwfB9JfAsBALSmIXk9NDTjfEvfnJRkffXtP6TKXTZVBbQf/GYHoFn2VvklsTspACC6/njWbXIGUyx9/y8zWcEv/p89BQHtCOEQQLPsqfZHjBuqgR4AAI5E57RMje/6I0vfSq9H7yx/Uaops6kqoH0gHAJolj1VfgYOAQAx8ftx1ykpkGvpezorRWUbv7SpIqB9IBwCaJY91b6IcUKDkUMAQBQkuT26+ujbLH3FLpf+sGCqPQUB7QThEECz7K3yywwfOiQbAgCi5OdjzlM/X6ql74PQSq3dWWRTRUDbRzgE0CyVvmADvaRDAEB0OBwO/bLrWZa+WoepX82cYlNFQNtHOATQLIO7ZkSMHPqDIZuqAQC0B6cdc57OK6+09K2v/VhzNn5nU0VA20Y4BNAsl4/qIZfTGg6ragMKhRg9BABESadB+vmeMnlDB/4YaRgh3TvncRuLAtouwiGAZslMcatHjnXtRzAU0vRFW22qCADQ5nlS1DW7r64qK7d079YivbRolk1FAW0X4RBAs3XJSo7oe+KjVaryBWyoBgDQLuQN0/V7ypQdtK59//OiPyoQbGg9PIDDRTgE0GwOw/otw5BUXFarv322wZ6CAABtX5dhSjdN/XT3Xkt3rXOzHpnzH5uKAtomwiGAZos857DO3+esV9He6liXAwBoD/KGSZIuKa9QL5/fcum/G/6hvTVVdlQFtEmEQwDNZ4Sdc7gvLtb4Q3riw9WxrwcA0PblDZUkuSXdtnuP5ZLpKtXkGX+NfU1AG0U4BHDYDo6K0xdt1fKtexu9FwCAw5KSI2V2lySdXlWtETU1lstf7XxNm0p32FEZ0OYQDgG0gHGIlvTJSn44AwCiYN/UUkPSr3btsV5z1ui2GVNiXhLQFhEOATRf2LRST9i5h9V+do0DAERBl2H1Xw71+TS22m25vLbmI325eWWsqwLaHMIhgOZzWH8YO0M+S7tTujeW1QAA2ou8YZbm3Xt3ygw569uGEdJvZj8R66qANodwCKD5ktItTa9ZK48O7BzXKYNwCACIgi7WcNi9eq+Ge8da+nZpob4uXBfLqoA2h3AIoPmSMi1NQ6a6GTvr253Sk2JdEQCgPcjoJiXnWLr+0H+0FLL+3Pl809JYVgW0OYRDAM3njBwZ7G4c2ISGaaUAgKgwjIjRwy5lG+Q1u1j6CsuLYlkV0OYQDgE0m7nvXMODdTdK6r9mWikAIGrC1h2qaKnSnNbRxO2VxTEsCGh7CIcAjsj+cJjmdSnF47K5GgBAm9XlGGt7+1JleTtaunbV7BSAw0c4BHBE8vdNK2VKKQAgqsJHDiuK1dWdaukq8xMOgSNBOARwRPaPHHYkHAIAoim3r+ROsXT1d/gt7epQaSwrAtocwiGAw2bowIY0nTLYqRQAEEUOp9R5iKWrf3CvpR1w7IlhQUDbQzgE0GymGbkhTY5RoTRVMa0UABB9YTuW9q3abmkbjloVV1gDI4DmIxwCOGLdjRLCIQAg+sLWHfbYvTbilpU7CmJVDdDmEA4BHLHuxg6OsQAARF/YyGHq7k1S0LqsYX3p1hgWBLQthEMAh83Y99+6kUPWHAIAoqzTIMlhPTYp1Uy2tDftKYplRUCbQjgE0GymItccSlI+00oBALHg8kodj7J0dTSdlva2iuJYVgS0KYRDAEesu7GDkUMAQGyErTvMDwUs7Z3VO2JZDdCmEA4BHLEejhJlJLuavhEAgCMVtu6wt7/M0t7t2xnLaoA2hXAI4Ih1NUplGEbTNwIAcKTCRg571lgPvq8M7oplNUCbQjgEcNj2x0GfgymlAIAYyRtqbQZ8lrZPu2NZDdCmEA4BNFtjG9JUuHJiXAkAoN1KypCye9c3OwWDlsumo1w1fl/4UwCagXAI4IjVenPtLgEA0J4ctO6wU8AaDg3D1Jpd22JdEdAmEA4BHLFASke7SwAAtCcHrTvMDoXkDJvYsqZka4wLAtoGwiGAw7Z/zaGR1snWOgAA7UyXY+q/dChy9HDjHkYOgcNBOATQbKbZ8JpDV0bnGFcCAGjXwnYs7Ry0nnVYULY9ltUAbQbhEMARS8rqYncJAID2JL2zlHbgD5OdA9ZwWFxVHOuKgDaBcAjgiKV36Gp3CQCA9uag0cPwHUtLa3bGuhqgTSAcAjhs+9ccpucSDgEAMXbQjqWdw9YclgcIh8DhIBwCOGKOdNYcAgBi7BAjh9Wh0lhXA7QJhEMARyQoh5ScY3cZAID25hBnHQYdexQKhWJdEZDwCIcAjsheR5bk4FsJACDGsnpJ3gxJUqew3UoNh1/byvfEviYgwfEbHYAjUu7MtrsEAEB75HBIeUMlRU4rlaRVJQWxrghIeIRDAIfPlCpcTCkFANhk37pDryllhQXE9aVb7agISGiEQwDNZsqM6Kv05NpQCQAAqh85lCLXHW7eWxTraoCERzgEcESqPYwcAgBscvBxFmEjh0UVxbGuBkh4hEMAR6TW28HuEgAA7VXHoyV3iqTIkcOS6h12VAQkNMIhgMNmyFRtEuEQAGATp0vqdpwkqXPYjqV7/bvsqAhIaIRDAEckQDgEANip+2hJkSOHVUHCIdBShEMAzWaGIrcKD6V2tKESAAD22R8Ow9Yc+rTbjmqAhEY4BNB8/uqILsIhAMBW+SMlRY4cylmhytpaGwoCEhfhEEDz+assTVOGjBSOsgAA2CglR+owMGK3UklatbPQhoKAxEU4BNB8YSOHtfIo2eO2qRgAAPbpfrwyQyF5QtbzeNfu2mpTQUBiIhwCaDYzbOSwxvQo2cO3EQCAzbqPlqHIHUs37i6ypx4gQfFbHYDmCxs5rJZHSW6nTcUAALBPIzuWFpYTDoGWIBwCaL6wcFgjr5IJhwAAu+X2k5KzI3Ys3VG1w6aCgMREOATQfA1MK2XkEABgO4dDyj9encNGDktrS2wqCEhMhEMAzWZGjBy6GTkEAMSH7sdHjBxWBHbZVAyQmAiHAJotMhx6lewhHAIA4kD30eoUsG5IUxsqtakYIDERDgE0XyA8HDKtFAAQJ7qNUKeQtSvk2K1QKNTw/QAiEA4BNE8oJCNQa+mqMd1KcvNtBAAQBzypysnsa+kyHSFt2bPTpoKAxMNvdQCap2aPJOvhwj555HHybQQAEB86dx8jw7T+rFq1s9CmaoDEw291AJqnKnJRf8DhkWEYNhQDAECkpJ4nKCdonUa6vpRwCDQX4RBA81TtChs3lAyny5ZSAABoUPfRETuWluxYblMxQOIhHAJonsrINRsug28hAIA4kpmv7KD1Z1PFnhU2FQMkHn6zA9A8DUwrdTqZUgoAiCOGoWRHjqWrumaLTcUAiYdwCKB5qhoYOXQQDgEA8SU5qbulXcNZh0CzEQ4BNE9V5A9Xl4NvIQCA+JKWdbSlvdfhb/BnGIBI/GYHoHkaWHPoJBwCAOJMh87HWto7XE6p8GubqgESC7/ZAWiehtYc8h0EABBn+nfsZWnvdjpVufFLe4oBEgy/2gFongbXHPItBAAQX47qmB/RV7Rlrg2VAImH3+wANE8DI4cudisFAMSZvLQsOULWc3h3714lBf02VQQkDsIhgOap3KWqsHMNvU6PTcUAANAwh8MhZyjL0rfTCEjFy+0pCEgghEMATfNXS/7KukX9B+mY0tGmggAAaJzHkWtpFzudUsECm6oBEgfhEEDT9k0pLXZaw2HXtM52VAMAwCFlua0/n5Z5PVLBfJuqARIH4RBA0/aFw/CRw95ZXe2oBgCAQxrRaYSl/XVykoKEQ6BJhEMATavcqQrDUFXY7qT9c7vZVBAAAI27aNCplvZup1Prq4qlvVttqghIDIRDAE2rKo0YNZQa3i4cAAC7jczvJ0cgx9L3dbJXKmTdIXAohEMATavaGbHeUMEUZSWn2lMPAABN6OIdYmnPT0piUxqgCYRDAE2r2qUdLuuZUS4zy55aAABohjFdR1vaC5OSFNgyz6ZqgMRAOATQtMqd2hE2cpjizGnkZgAA7HfJoNMs7XKnQ6t3raw7nglAgwiHAJpWtUvFYWsOM9wdbCoGAICmDcnrIVfA+rPqa69L2rbIpoqA+Ec4BNC0ql0RI4cdkjraVAwAAM3TLfkYS3tBchLnHQKHQDgE0LSqXRG7lealdW7kZgAA4sMJXY+3tL9J8qp2M+sOgcYQDgE0rYE1hz0y8mwqBgCA5rlk8GmWdpXDoeXbF0qmaU9BQJwjHAI4tFBI/upS7QoLh31yutlUEAAAzTOwY1clBazLIBY5fNKudTZVBMQ3wiGAQ6vZo10OQyHDsHQf1SHfpoIAAGi+rinHWtrzk70SR1oADSIcAji0BtYbmiGnemd3sqkgAACa7+T8MZb2Yq9X1Zu+sKkaIL4RDgEc2t7CiPWGjlCmHA6+fQAA4t/3B58qHbTEsMbh0KJCRg6BhvDbHYBD27km4ozDJCPbpmIAAGiZXjmdlBa0rjtcEiqVKkpsqgiIX4RDAIdWsipi5DDNlWtTMQAAtFzX1JGW9oKkJKmA0UMgHOEQwKGVrNYOl8vSle3t2MjNAADEn1N7nGRpL0nyqmIj6w6BcIRDAIdWsjpi5LBTCpvRAAASxyVDxso4aN2h3zD09ebP7SsIiFOEQwCNq9wlVe2MCIfd0vNsKggAgJbrmpGjrLB1h0t92yRflU0VAfGJcAigcTtXy5QiNqTpldXFnnoAADhM+WmjLO2FSR5p27c2VQPEJ8IhgMaVrFKFYag67NiK/jn5NhUEAMDhGdv7VEt7udejPes/s6kaID4RDgE0rmS1doSNGkrSUZ262VAMAACH75JBJ8tx0LrDgGHoqw2f2lcQEIcIhwAaV7JaxU7rTqUKpigzKcWeegAAOEwd0zLUIWBdd7i8ZpMUCtlTEBCHCIcAGtfAyKHLzLapGAAAjkyPdOt5h996HVLJSpuqAeIP4RBAw2r2SuXbInYqTXHm2FQQAABHZmzf8Zb2So9HO9Z8YlM1QPwhHAJo2M61khQxcpjpzrWjGgAAjthFg0+S66BZpCHD0BdrP7avICDOEA4BNKxklSSpOGzkMDepkx3VAABwxLKSU5UX7GDpW169zqZqgPhDOATQsJLVkqRil3VDmi5pne2oBgCAVtEr/VhLe6k7KO3dalM1QHwhHAJoWMlq1RiG1nrclu5+Od1tKggAgCN38sDzLO3VXo8KV82wqRogvhAOATSsZJWWej0KGEZ9l2ka+t5RY2wsCgCAI3PB0SfKE3Z6xWerP7CnGCDOEA4BRPJVSXu2aGFSkqXbG+quvHSOsgAAJK5Ur1f5QevO299VrLGpGiC+EA4BRNq1VpKphUleS3ev1KH21AMAQCvqkzbc0v7OXSPVlttTDBBHYhoOQ6GQAoFAi54JBoMtfgbAESpZLZ+kpV6Ppfvk/NH21AMAQCs66ajzLe0NHrfWffehTdUA8SPq4XD+/Pm64IILlJubK6fTKbfbrX79+unRRx9VbW1tg8989NFHmjBhgjIzM+VyueT1ejV06FA988wzMk2zVeratGmTDMNo9uuBBx6IeI9vv/1WF154oXJzc5WSkqKTTjpJH330UavUB9iqZLWWe72qdRz4FmGahi4ZfIqNRQEA0DomDT5ZSWHrDmev+J89xQBxJKrh8IUXXtBJJ52kt99+W9XV1RozZoz69u2r9evX65577tGECRPk8/ksz/z+97/X2WefrY8/rjuQ9MQTT1TXrl21fPly3Xzzzbr88stbpTbDMOT1ept8Gfs243A4rP9TffjhhxozZozeeustlZaWqqamRl999ZXOOeccTZ06tVVqBGxTsipiSqk72FXds3JtKggAgNaT5PaodyDL0reqYpU9xQBxJGrhcMmSJbrxxhsVDAZ1xx13aMeOHZo7d67WrVunZ555RoZh6PPPP9eUKVPqn/nggw903333yel06vHHH1dJSYm+/PJLbdq0Sffee68k6bXXXtMrr7xyxPX17NlTNTU1h3ytWbNGSUlJcrvduuyyy+qfXb9+vS655BL5/X4dd9xxWrJkiQKBgD766CNlZWXp5z//uTZt2nTENQK2KVmtb8LCYU/WGwIA2pA+qUMs7TXOCinot6kaID5ELRxOnjxZPp9PkydP1pQpU5SWllZ/7aabbtJFF10kSXr55ZclSaZp6vbbb5ckPfXUU5o8ebI8nrr1Tk6nUw899JBGjhxpeSba7rrrLlVXV+uWW27RgAED6vvvueceVVZWqmvXrpoxY4aGDRsmh8OhM888U3/+859VWVmpX//61zGpEWh1AZ98pRu0KCwcnth1lE0FAQDQ+kYNmGRpb/S4tHntHJuqAeJDVMJhZWWlUlJSNGrUKN1///0N3jNqVN0vmlu3bpUkFRQUqE+fPpowYYJuuummBp/ZHw73PxNN8+bN06uvvqpOnTpZ/g0lJSV64403JNWFx5wc61bIl19+ubp27ap333230TWVQFwrXa9VHqeqw6ZSX8x6QwBAG3L20PHyhqx7WcxeNt2maoD4EJVwmJqaqunTp2vBggVKTU1t8J5t27ZJktLT0yVJPXr00HvvvacZM2bUr/Nr6ploMU1Tt956qyTpkUceUUZGRv21L7/8UsFgUJL0/e9/P+JZl8ulCRMmqLKyUnPm8NcnJKCS1RHrDZ2BPPXNzbOpIAAAWl+q16uefuvvqStLF9tTDBAnXHZ8aDAY1FtvvSVJGj9+fLOeKSsr08yZM1v0zOF69913NX/+fPXt21c//OEPLdfWrl0rSerdu7e6dOnS4PPDhg2TJK1atUoTJkxo9ucWFhYe8npRUVGz3ws4bFvm6pukJEtXfvJgm4oBACB6eiX11xpzSX17vVEqmabUyEAF0NbZEg6ff/55bdmyRYZh1I/QNeWJJ55QdXW1kpOTG5122loeffRRSdKdd94pp9NpubZr1y5JsqxBDNepUydJ0saNG1v0ud27d2/R/UCrM00FVr2rb7OsI4djuh5vU0EAAETPsb3O1IyNB8LhWo9DO7cuV4d8NmFD+xT1cw7DFRUV6a677pIkXXfddRo+fHiTzyxbtkx/+MMfJNVtBpOXF73pbbNnz9bcuXOVl5cXMWooqX4dYVZWVqPvkZ2dLYmRPiSgosVaU12sirD1hhcePdamggAAiJ6Jx10o50FnaAcNQ7O+fc3GigB7xXTkMBQK6aqrrlJpaal69+6tJ598sslnqqqqdNlll8nn82n06NH1wTJa9o8a3nbbbfJ6vRHX948kJicnN/oe+3dZra6ubtFnFxQUHPJ6UVGRjj+eERxE0cp3I6aUOgIdNLgzo9oAgLYnOzVdPf0ebfAcOMJi6Y75+oGNNQF2imk4vO+++zRr1iwlJSVp2rRpyszMbPKZG264QStWrFBubq5effVVud3uqNW3du1azZgxQ263W9dff32D96SkpEjSIetw7Bt1qaqqatHn5+fnt+h+oNWtejdiM5quSUMauRkAgMTXy9VTG7Suvr0huN3GagB7xWxa6bRp0/TII49Ikp599tn6YykO5YknntArr7wil8uladOmqVevXlGt8bnnnpMkTZw4Ubm5uQ3es//oiv07pzZk9+7dkup2PQUSxs51CpWs0jdh4fD4vKb/vwoAQKIa3MW6dGKNJ6TyPY3/nge0ZTEJh/Pmzatfv3f33XfrmmuuafKZ6dOn6+6775YkPf300zrjjDOiWaKCwaBefPFFSdLVV1/d6H09evSQdOjNZrZvr/uLU1paWitWCETZqne0zu3W3rBNmL531Mk2FQQAQPSdM+pyGQf9Qb/W4dCnX//HxooA+0Q9HK5YsULnnXeeqqurdemll+rhhx9u8pnPPvtMV155pUKhkCZPnqyf/OQn0S5TM2bM0LZt25SVlaWJEyc2et/+Ec+1a9eqvLy8wXvmzZsnid1HkWBWvhsxamgEcnRct742FQQAQPR1z+2ifL/1D6OLCzmrGu1TVMPhd999p3Hjxmnnzp067bTT9MILLzR6wP1+s2fP1qRJk1RdXa0rrrhCjz32WDRLrPf6669Lks4+++wGN6LZr0ePHhowYID8fr/eeOONiOvBYFCffvqpJGnEiBHRKRZobWXbpK0LI9Yb5nkH2VQQAACx09uw7oS/3rfFpkoAe0UtHBYXF2vcuHHavn27hg4dqrfeeuuQoUuSli9frkmTJqmiokLjx4/X1KlTmwyTrcE0TX344YeSpNNPP73J+6+99lpJ0u9+9ztVVFRYrr3wwgvatm2bnE6nzjrrrNYvFoiGVe8pJEXsVDqiE3/gAAC0fUd1sO4Gv9rjk6+64RliQFsWtXA4ZcoUFRcXS5JWrlypzp07KykpqcHXnDl1Q/cPPvigKisrJdVNLU1PT2/0mc2bN9d/1ubNm+v7X3rppRbXumjRovozCZsTDm+88UZlZWVp48aNmjBhgpYuXarKykr961//0s9+9jNJ0uWXX64uXbq0uBbAFivf0acpydrlCltvOJDzDQEAbd+4EZdZ2pUOh75g3SHaoagdZbF48eL6rwOBgAKBQKP3hkKhiGf8fn8jd9c5eCdQ0zTrD6cPBoMtrvWDDz6QJHXr1k39+/dv8v4OHTro5Zdf1sUXX6x58+bpmGOOsVzv2bNns85wBOJCValCm77Qc3kdLN3uYL7GdB9gU1EAAMTOoB6D1dkvFR90Utm36z/UGafcaF9RgA2iNnI4c+ZMmabZrNdpp50mqW6Tl+Y+c/CxFr169arv378rakv85je/kWmaKiwsbPYzEydO1FdffaWTTz6wk6NhGLrwwgs1d+5cderUqcV1ALZY85EWeVxaGrbe8ILeV9af2QkAQFvXQ2HrDmvXNXIn0HZFbeSwPRgxYoQ+//xzbdmyRVu3blXfvn0JhUg8q97Vc1kZli4jkKNfnfx9mwoCACD2+uWO0ddlb9W3V7oDCpVtlyMjr/GHgDaGYYFW0KNHD51wwgkEQyQeX5VWb/5Uc1KSLd2n5l2sFPehN5ACAKAtOe2YH1jau1xOLfrmNZuqAexBOATas/Wf6MXUsLMNg8l64PQf2VQQAAD2GNNrsDIC1l+NF274yKZqAHsQDoF2bOOS1/R+Wqql75jMScpNSbepIgAA7OFwOJRvWqeQrqzdKB20CSLQ1hEOgfYq6NerJfMVOOgsUWfIoQdOY2c2AED71L+j9QinBUkOVRUts6kaIPYIh0A7VfTtNL2V5rH09fOcpL65LLwHALRPlx1/taVd7nRoxryp9hQD2IBwCLRHoZBeWzBFVQcdVWGY0p1jb7OxKAAA7DWkS0/l+5IsffN2fGVTNUDsEQ6Bdqhs+XRNT6619A0I9dboHv1tqggAgPhwdPIQS3uRa69CvmqbqgFii3AItDemqTc/f1ilTqel+9ox99hUEAAA8WPCsKss7W1ul75aOM2maoDYIhwC7Yxv5bt63Vtl6cv35+m8QWNsqggAgPhx1pDTlRuw9n2yero9xQAxRjgE2hPT1Puf/k5b3G5L90XDWGsIAIBUd6TFILOrpW+Vb4NN1QCxRTgE2pHQqvf1mnuvpS/Hn6XrR55tU0UAAMSfY7uebmmv8oZUUrLZpmqA2CEcAu2FaerzWb/Tcq/X0n1m35/I4eBbAQAA+513wo/kNM36dsAw9MHcf9pYERAb/EYItBOh1R9qqrPU0pcaSNEdJ//ApooAAIhPedmd1c9nPQt40fYvbKoGiB3CIdAemKZenXW/FiZbz246sctVSnJ7GnkIAID2a4B3gKW92rFDOmg0EWiLCIdAO1Cw+HU9k1Rp6UsJJOu+035kU0UAAMS3kwZeZGkXuB1asvJTm6oBYoNwCLR1pqk/zXtUe8LONbzyqF8rOyXNpqIAAIhvE467UBnBkKXv48Wv2FQNEBuEQ6CNe2PmnzQzxXpgU/9Avm456aJGngAAAB63WwMDWZa+FeVL7SkGiBHCIdCGlddU6e9bplr6kkOmfnfO/9lTEAAACWRw1khLe6W7Ur7aapuqAaKPcPj/2bvv+Kaq94Hjn4zu0hbKKJSypwjKLEMQFQQZypQ9Bb5sQRFliAoOQEFRhgrKRlAREBVkyJah7CF7j5Yyukea5P7+6C+XXprutKH0eb9efdl7cseTK03y5JzzHCEeY+//PIybLtrJ823dGvBkiXJOikgIIYTIO1rU7aPZjjLo+XPfMidFI0TOk+RQiMfU7rP/si3xH01bpQQrb3f80kkRCSGEEHnLk2VrEWTStu2/8LtzghEiF0hyKMRjyGq18tmOkSTqdWqbXlEYVmEQrm4eToxMCCGEyFsq6QI122fMl5wUiRA5T5JDIR5Ds/78gguuUZq2NvEFeP65kU6KSAghhMibagU+p9k+52rhZthl5wQjRA6T5FCIx8z9mCjW3lykaStmNjO69deg09k/SAghhBB2tW34Gq7WB/P3LTodv/39nRMjEiLnSHIoxGNm8i/DuWfUFqHp4x5M4aCnnBSREEIIkXcV9C1MpURXTdux27udFI0QOUuSQyEeI4cuHWOn+aCm7el4Kz06zXZSREIIIUTeV8W9kmb7jC4Mq9XqpGiEyDmSHArxGPl8ywhMDxWhGVRlKHo3bydGJYQQQuRtjSt31GyHuOj4978dTopGiJwjyaEQj4kfdizkiOs9TduLCd40bjLUSREJIYQQj4emddpTyKztKdxydLmTohEi50hyKMRjID7RxNKzszRtvhYrr7eYLUVohBBCiGzSG4xUsfhq2v6LOuakaITIOZIcCvEY+GzdBK65WjRt7Y1PUrJMHSdFJIQQQjxeqhesq9k+4xpLbEKsk6IRImdIcihEHhcSEcbGyA2atnImCyM6feukiIQQQojHz0t1+2m24/Q6ft+7zEnRCJEzJDkUIo/7eO1wIgzaoaN9inXA1dM3lSOEEEIIkVnlyzxN+QTtUlEHLv3hpGiEyBmSHAqRhx28dIzd1pOatuA4HR3aTHZSREIIIcTjq5K+hGb7nPmycwIRIodIcihEHvbFllEkJlu6wqgoDHxqDOjlT1sIIYRwtFqBz2u2L7mYuXb7qpOiEcLx5BOkEHnU6v1rOOIapml7Mb4AwQ16OykiIYQQ4vH2UsN+uFkfDC216nT8se87J0YkhGNJcihEHmS1Wll07CNNWwGLlWHNP3dSREIIIcTjz9e3GJVNLpq2Y6G7nRSNEI4nyaEQedDnGz7lsmuCpq0dZSlVvr6TIhJCCCHyh8ruFTXbZ3WhTopECMeT5FCIPCY8Npr1Ics1bSUTLQxr/42TIhJCCCHyj4aVXtZsh7joOHTmbydFI4RjSXIoRB7z8do3uWvUltLuXqAJXgUDnRSREEIIkX80rfMqfharpm3z4aVOikYIx5LkUIg85EzIFbYn7NG0PRVvpUf7mU6KSAghhMhfjC6uVEksoGn7L/KIc4IRwsEkORQiD/nsjxHEJVu6QqcovFauL3pXTydGJYQQQuQvVX2e1myfNUZiSjQ5JxghHEiSQyHyiD1n9vGP/qKm7fl4F55rNsZJEQkhhBD5U/NaPTXbUQY9m//52UnRCOE4khwKkUd8vfNtLLoHvYZuViv/a/A+JGsTQgghRM6rXvkZSiZq5//vPbfOSdEI4TiSHAqRB/y6/xeOuN7TtLU0+VP1qVecFJEQQgiRv1VWimi2zyacc1IkQjiOJIdCPOKsVitLjn6safOxWBnccpaTIhJCCCFEjWKNNdvnXU3cjbjtpGiEcAxJDoV4xC3d8hVn3LQL3reiAiVL13RSREIIIYRo1aA/BuXB0NJEnY7f//7eiREJkX2SHArxCDObE/npyneatqJmC8Paz3NSREIIIYQACChShgomg6bt6I2/nBSNEI4hyaEQj7C5a9/hiqt2wvvLng3xK1jCSREJIYQQwqaiS3nN9jnlJihKKnsL8eiT5FCIR1RUzH1+Dd+oaSttUhjS4UsnRSSEEEKI5OpVeFmzfclVx9lzfzspGiGyT5JDIR5Rs1cPJ9RF+yfaIaAjrm6y4L0QQgjxKGgR3BVPq7ancMuhRc4JRggHkORQiEdQaOgF/jAf0bRVTjDQt9Uk5wQkhBBCiBQ8Xd2pkOijaTsVfthJ0QiRfZIcCvEI+vK34YQbtH+e3auMRG8wpHKEEEIIIZyhkm9tzfZJl1is8ZFOikaI7JHkUIhHzNkL+9ikv6ppezrBiw5N+jspIiGEEEKk5rmavTTbd4wG9u5f5qRohMgeSQ6FeMTM2/oW8foHf5p6RWFA8GQnRiSEEEKI1DxToQ6FzDpN277zvzkpGiGyR5JDIR4hBw6tZbvrfU1bcGJRnn3qRSdFJIQQQoi06PV6ylFS03Y68bIsaSHyJEkOhXhUKAoL/pmCWffg20dXq8KwZjOdGJQQQggh0lOtRHPN9lE3HbHXpTCNyHskORTiEfHnjnnsdTdp2hpTgafKPu2cgIQQQgiRIe3rdtVsx+n1bD+wyDnBCJENkhwK8QiwWswsOfO1ps3bovBmm9lOikgIIYQQGVW+cHFKmNw1bUdu73VSNEJknSSHQjwCfvr9fY65a+cmPO9ah6AiJVM5QgghhBCPktLu1TTbJ/QREHc/lb2FeDRJciiEk5kTYlgZukbT5m9WeKv9l06KSAghhBCZVbdie832STdXbh5b56RohMgaSQ6FcLLZPw/nvKv2T/El35fw8/JxUkRCCCGEyKzOTzXDxfpg26rTsfnET84LSIgskORQCCe6dOscPyUc0LQFmvS8/vKHTopICCGEEFnh5+FFKUsxTduhhPNgtaZyhBCPHkkOhXCiT34bQqRB+2fYpfRruLu6OSkiIYQQQmRVjWLadYn3eRiJvvK3k6IRIvMkORTCSdb98yt7XUM1bXUTPOn34kgnRSSEEEKI7OhRvyc65UGBuVi9nj2bxoPF7MSohMg4SQ6FcAKr1cr3R7RDRz2tVsY8O8NJEQkhhBAiuyoXKUFgYgFN237TVdgxzUkRCZE5khwK4QRzN8/momucpq29tQxPVH7GSREJIYQQwhHK+LfSbG/39MCy81O48JeTIhIi4yQ5FCKXxSYmsPbad5q24okWhrWb66SIhBBCCOEo3Wp20GyHGY2ccnWB1QMh8paTohIiYyQ5FCKXTV0zjlAXbeWyLh71KOBf2kkRCSGEEMJRmpSthsFcVNO2zcsDYu/A6gEy/1A80iQ5FCIX3Yy4w5bozZq2qgkW+nWUBe+FEEKIx0Uln/qa7b88PZJ+ubJb5h+KR5okh0LkoqlrhxNl0Lb1KtERvbsseC+EEEI8LjpWbqnZvuDqylWjMWlD5h+KR5gkh0LkkiNXTrLbekLT1iBOR9tW7zsnICGEEELkiI7VGoJFW7V0m633EEXmH4pHliSHQuSSLza/TqJep24bFYWBT70JekMaRwkhhBAirzEaDAS51da0/eXl8WBD5h+KR5Qkh0Lkgj8O/cFBF+2C983jvanboI+TIhJCCCFETmpZtplm+7CbO/f0yT56y/xD8QiS5FCIHGa1Wvnu3w80bd5WK0Oe/9RJEQkhhBAip/Wu2QzF6qpuKzrY7Beg3UnmH4pHjCSHQuSw77bO5axbrKatjbUUZSs1dlJEQgghhMhpfh5eFNZX17R96lWQK65uyVpk/qF4tEhyKEQOijcl8POV+Zq2YmYLw9p97aSIhBBCCJFbni35nGY7wRhFp4CSnHB90KNI7B34ZSBYLbkcnRApSXIoRA6asW4CNx9a8L6jRzB+suC9EEII8dgb88yrGM3FNW3xhkT6FA9gl4f7g8bLu2T+oXgkSHIoRA4JjbzHxsg/NW0VEqwM7PCVkyISQgghRG4q4ObBirbf42IuqWk36WF4sSKs9fZ60LhjOlzYlssRCqElyaEQOWTqmhGEG7VtvYp3wOju7ZyAhBBCCJHrqhYtyR+vrsDbWlXTbtXpeLeIP9/6+qAASfMPB0DICXunESJXSHIoRA44cf0su6xHNW1143R0aP2+cwISQgghhNMEFCjIlu5LCdA3TPHYV4X8+Mi/IBZImn+4sBVc+TvXYxQCJDkUIkfM+PN1EpIteK9XFAY8OVIWvBdCCCHyKS83NzZ0n0tVj5dTPLbKpwBvFC1MvE4HCRGwtD2c2eCEKEV+J8mhEA626fh2DhquadqaxXvQsNFrzglICCGEEI8Eo8HAj69+xLP+A1AUneaxv7w8GRRQhAi9HszxsLIHHF7upEhFfiXJoRAO9s2+CSi6By/4HlYrg5t8BDpdGkcJIYQQIr+Y3eZ1upcdh2LVjig67O5O7+LFuGUwgGKBdUNhzywnRSnyI0kOhXCgRTuXctY1UtP2cmJRKj7xopMiEkIIIcSjaPyz3RhT4zOwuGvaL7q60LNEMc64uCQ1bJ4Em94FRXFClCK/keRQCAcxWyz8eOYLTVths4WhbWY7JyAhhBBCPNL61m7GZ42/RWfx1bTfNhrpW6IYB9zdkhr+/hLWDQOL2QlRivxEkkMhHGT2nzO55mrStHU0VKFQiWpOikgIIYQQj7oWFWuy5KVlGM0BmvZovZ7BAUXZ6OWZ1HBkOazqCYlxTohS5BeSHArhALGmeH67uUzTVspkYUD7eU6KSAghhBB5xdPFy7Cu4wo8LBU07Yk6HW8VLcwSnwJJDWc3JFUyjQvP/SBFviDJoRAO8Nm6iYS6WDVtrxZohLtvMSdFJIQQQoi8pJRfETZ3X44/tVM89ql/QT4t5IcZ4OpeWNQaokJyPUbx+JPkUIhsuhsdzubIPzVtVRIs9Gr/hXMCEkIIIUSe5OvuyaYeCyjnmrKQ3RJfH7qWCOCYmyuEnoA5wbB3DpgTnBCpeFxJcihENk1b+zrhRm1bj4CO6N28nBOQEEIIIfIsV6ORNV0+pZ5vzxSPnXFzpWfxYnzoX5BIUwT8OR5m14UTv0g1U+EQkhwKkQ2Xw66xI/Ggpq12PLRr875zAhJCCCFEnqfX6/mu3du8EvgGiqL9uK7odKzyKUDbkiX43csTJfwK/NwPFjSDK3udFLF4XEhyKEQ2zFg/nFi9dnH7PpWHgN6QyhFCCCGEEBnzYbN+vFtrNkZziRSP3TMYeKdoYQYGFOWy0Qg3/oWFLWFlD7hz3gnRiseBJIdCZNGhi4fYo7+gaWsc58pzzw5xUkRCCCGEeNx0qdGYPb1/pUHB3ihWlxSP7/dwp0PJ4sz18yVBB5z+DeYGw+9jIOZO7gcs8jRJDoXIojlbx5Coe9BraFQUBtabBDpdGkcJIYQQQmSOp4sb3778Foua/4if8lSKxxN1OuYV9KVDYHH+dncHqxn+mQ+znoadn4EpNveDFnmSJIdCZMFfRzbyj8ttTdsLJj9q1nrFSREJIYQQ4nFXp2QFdvReQt/y76Oz+KV4/KqLC/8rXpQuJYrxQwFvIszR8NcUmF0HjqwAqyX3gxZ5iiSHQmTBdwfeR0nWQ+hptTKk2QwnRiSEEEKI/ECv1/PmMx3Z+urvVPZok6JgDcApNzc+LlyI54NK8lYRf/5OvItl7RD4pgmc2yyVTUWqJDkUIpN+3r2EY24xmrYXraUoXy7YSREJIYQQIr8p4u3Dz69+wvQG3+NmKWt3H5Nex0ZvL/4XUJQWQSX4KuEa11Z1hUVt4Pq/uRyxyAskORQiE6xWK8tPfaFpK2ixMvzlOc4JSAghhBD5WqvKtdnXZw0tig1DZ/FNdb9Qo5FvC/rSKqgE/RLO8uvKl4la0Q3unMvFaMWjTpJDITJhwZ8zOe+WqGlrbXiSYkXKOSkiIYQQQuR3RoOBz1oO5t/e2xlS5RMK6+qiWFNfVutfD3cmFPGnecIxJq1swbZl3bFG3MjFiMWjSqcoMug4r7h+/TpBQUEAXLt2jZIlSzo5ovzFlJjIy4vqcMPVqrYVT7SwpstOvAoUdmJkQgghhBBal+/dZubeVewO3UCi4Vq6+5c2mXne9Un+134OXj5FcyFCkV05kRtIz6EQGfT5rxM1iSFAe+/GkhgKIYQQ4pFTplBRvmw9gkP9/2Bq/YWUd2sJFo9U97/iamQhp+nwY1PmrBiAKT46F6MVjwpJDoXIgIi4aDaG/6FpK2+yMrD9506KSAghhBAiY1pXrsParp/yd48d9Cg7kYLWJ9ClMnbwpouBrxP302lpMAt+fR+r1Wp/R/FYkuRQiAz49Jc3uGPUtnUp8jJGN0/nBCSEEEIIkUkF3Dx4p0kXdvZbxYqWv/OMd3v8E13t7nvJVc+s+6tp9v3zLDy4OZcjFc4iyaEQ6bh+L4S/Ev7WtNWIhy5tJjspIiGEEEKI7HkyoBTzOk7mr/7/8HaZMZQ32U8Sw1zuMvPEGzRc2IW1p/blcpQit0lyKEQ6Pvt1JFEGnaatZ7l+6I0uTopICCGEEMIx9Ho9PZ/twy+v/cPIgNcIMtlPD6L0p3j3n4E0XdyP7RdP5HKUIrdIcihEGk7eOMNu5ZSmrUGcgZeajXZSREIIIYQQjqfX6xnYYhS/9jvIaKpQMjHR7n53+ZfhO7vTfuVYTGZzLkcpcpokh0Kk4YuNo0nQP+g1NCgK/Z9+C3S6NI4SQgghhMibjEYj/Xv/yJqAVrx75x5F7CSAOp3C+YQN9Fv3oRMiFDlJkkMhUrHnzF7+MVzVtD0X70n9+j2cFJEQQgghRC7Q6XBvPYNXK3fh9+u3GH3vPj4WS4rdjkau41jI5dyPT+QYSQ6FSMXcneOwJOshdLNaGdRoihMjEkIIIYTIJTodtPoMj1p96B8RxYbrNxkYHoFBebAGhk5v5u2tnzkxSOFokhwKYcfiPSs55npX09bS5E/V6i2cFJEQQgghRC7T66H151CzFz5WhZH3I+gYFa3Z5VriTv48d9hJAQpHk+RQiIeERUey4PR0TZuPxcqQF2c6KSIhhBBCCCfR66Htl/B00rSaIeEReFit6sM6ncKUPZ86KzrhYJIcCvGQkWvHEm7UVujqTFkCy9ZxUkRCCCGEEE6k18PLX0GNrhS2WOkXEal5OEJ3nO//3eSk4IQjGZ0dgBCPkuVHt3PSvAeSFSOtGW9ieJevnReUeCyZTCaio6OJiYnBZDJhTfYtrBBC5CSDwYC7uzs+Pj54eXmhkwrcIiP0hqQE8dZR+tw5zaoCBbhrNKgPzzk2i941X8BoMKRxEvGok+RQiP8XHhfDF/9OQkn2V+FmtfJW0RYY/Uo6LzDxWFEUhTt37nDnzh1nhyKEyKfMZjMJCQlERETg4eFBqVKl0OtlMJnIAKMrvPwlnt+9yNDwCKYULqQ+ZDJc5eOdPzDpuZ5ODFBklySHQvy/Qb99TLzxvqZtSGQ81bt+4KSIxOPo1q1bREREaNp0Oh0G+aZVCJFLLBYLyv9XnIyLi+Pq1auULl1aehBFxgTVg3oD6XDgW5b6FOCyq4v60OqLCxjdsCMF3DycGKDIDkkOhQB+ObmX/2LWa4aTVo9PoPfTQ8CjoPMCE4+V+Ph4TWLo7++Pj48Pbm5u8qFMCJFrrFYr0dHRhISEYLFYiIuLIyYmBm9vb2eHJvKKFyZhPP07o+7fY1SxImqz1XiXdzZ/w5w2o5wXm8gWGUMg8r2YhASm7HsPdA/W7TEqCu9Fg0uDoU6MTDxuwsPD1d+LFi1K0aJFcXd3l8RQCJGr9Ho9Pj4+BAQEqG1RUVFOjEjkOW4FoPVMno+No2Z8vOahnbd/4HrEPScFJrJLkkOR7w3+bTpm4y1tW3gElRu/Ba6eTopKPI5iY2PV3/38/JwXiBBCAN7e3uqXU3FxcU6ORuQ5lVuiq9aeN+6Fa9sNsby5SZb/yqskORT52g9Hd3A46mdNW5UEE/0UH6jV20lRiceVxWIBwGg0yhxDIYTT6fV69bXI9vokRKa8NJ2ndZ40j4nVNJ+K/p3DNy85KSiRHZIcinxr2ZFtfHToTXS6B0sIGBSFyXfu4tpkLBjdnBidEEIIIcQjzrsovPghI++FY1QeTM9Bb+advz51XlwiyyQ5FE51MzyOVf9cZf7Oi9yKyL0hLWtP7WPq4TfR6RM07f0iIqnqFQhPd8+1WIQQQggh8qyaPSlTsgGdoqI1zTfNu9h49pCTghJZJdVKRa6LM1n4fs8l1h+9yemQBxPgF+y+yMbXm1DQyzXHY5i6fwY6faKmrUlsHEPvR0C7qWBwSeVIIYQQQgih0umg7Sz+900jfvX2Ita2ZqYOPtwznZaVVjo3PpEp0nMoclVkfCLd5u/j0z/PaBJDgNDIBJbtu5LjMcQkJBCtO6dpez4mli9Cw3DxrwDVX83xGIQQQgghHhv+5SnceCz9IiI1zRH6k8z/508nBSWyQpJDkWsi4hLptWA/R66FU0F3nW6GrZTT3dTss2TfFUxmaypncIxdV06i02kn3k+5cxcXgKbjwCAd6kIIIYQQmdJwBD1dgyhs1n7G+u7YdMxS8CjPkORQ5IrwWBM9F+zn6PUI2uj3ssF1HJ+4fMcfruNooj+q7hcWlcDvx2+mcabs23vtuGa7RKIZH6sCRapAtfY5em0hxOPt8uXLvPDCC3zyySfcunUr3f3ff/99Dh8+nK1rms1mfv75ZxISEtLdd9++fRw+fDhD+2ZVYmIiS5YsoVu3blIBU4j8xOCC9yuzGRqu7T2MMd7mo+1LnRSUyCxJDkWOux9jovv8/Ry/EUEj/XFmuszF5f977tx1icx1m0MpXai6/3e7L6Ekr3jlYCfvntZsVzKZkn5pOg70sryAECJ1ZrOZyMhIzGaz3cfv3LnDX3/9xfvvv4+LS9pzl69fv86UKVOoVasWgwcPznJMZ86coXPnzhQtWpTdu3enue9XX31FrVq1GDJkSKauYbVaWbBgASbb62UaLl68SL9+/Vi5ciXfffddpq4jhMjjAmvxSrXelDNp6zr8eXk2EfGxqRwkHiWSHIocdS/GRPcF+zl1K5IndRf5xuVzXB8a0umtRPONy0w8iAfgxI1I/r1yP8diuhFzQbNdyZQIxapD1Zdz7JpCCOfp168fOp3OIT8uLi74+vpy+vRpu9ey9RbWr1+fwoULpxnXsmXLsFqtuLq6MmrUqCw/v7179wJQsGBB6tevn+a+tp68J598MsPnj4iIoEGDBgwcOJCFCxemu3/lypV59dWkuduTJ08mMTExnSOEEI8T1xfeZUi8djmwKGMCk3//yEkRicyQyVUix9yNTqDHgv2cDomitC6Eha7T8dbF2923qv4a012+ZUTiCEDH97svUbdMIYfHZLVaiVW0RW8qm0zw4lugl+9KhHgceXt7A+Dj40Px4sWzfB5FUbBYLMTFxWE02n/7vHz5MgD16tVL81wWi0XtVXvrrbeoUqVKluPasmULAAMGDEg1ruTXBShVqlSGz+/r64tOpwPgk08+oX///un2io4bN46VK1dy48YNfvjhB3r37p3h6wkh8jgXD1585Vt+2NCLQ+4PksQ9Eb9y7d5IggoVc2JwIj2SHIocERaVQI8F+zgbGk0RwlniMpUiOu0YdAyuYHkwRKmtYR/HrOWYb2nDnydDuHYvlqBCng6N68ydm1gN2mENFRUXqNTSodcRQjw6XF2Tlsdp3749ixYtSnW/YcOG4ebmRteuXdNN7lJz4ULSyIRq1aqlud+yZcs4f/48FSpUYOLEiZrHNm7cSMuWqb8mXbp0CTc3N/V5bdq0CYBWrVoRHh6u7mcymbBYLJqE2DYstGjRohl/UsCUKVN48cUXuXLlCsuXL6dv375p7l+jRg1atGhBhQoVaNKkSaauJYTI+/Slgunn9yyH4vepbTEG+Gzd/5jVb63zAhPpkuRQONzle3fpseI7wsy3cfVXaGX4i026eMDnwU6FykDFlnBwEZjj8VSstIiO5R3jD5xSSrPHWp0ley8zofUTDo1t++Vjmm0Pq5VSZZ4Ho1sqRwgh8jpbr1darFYrP/zwA/fv36dr166ZOv/NmzfR6XS4u7vz33//AVCiRAmiox8sCK3X6/H0TPqyy2Kx8OGHHwLwzTff4O7uDiQVchk1ahRz585lzpw5DB061O71nnvuOa5cSbnsT+3atVO0vfDCC2rPou0akDQENTOaN29O/fr12bdvH59++il9+vTh2rVr1K1bFz8/P/z8/ChQoECK406fPs2AAQM0bYqiEB8fz/379yldujQbNmzIVCxCiLyhaYcvabKgLjs9H7wG7+Y8B6+eonYpx36+E44jyaFwqBhTAh3X9MNU4BJu//854RcA/B7aMxzOrQQfdyDpg9HXfr58GRrGV3zFy6aPWPmPkdebVcLbzXH/TI/ePKLZrmhKxFC7jcPOL4TImw4dOsT9+/dp2LBhpnsNW7VqxdGjRzVtLVq00GwHBwezb1/SN+hz587l/PnzDBgwgOeff17dx8XFBS8vLwBGjRrF008/TcOGDVNcLzg4mGrVquHi4sLx48e5ePEiTz75JJUrV1b3sVgsmEwmKleuzL179/Dy8sLFxUWtUurj45PivOl54403GD58OK+99hoWiwVFUQgPDychIYHw8HAMBgNWq5XQ0FBcXFzU3snY2Fju37+Pl5cXvr6+WK1WzGYzCQkJFCrk+OkDQohHhIsH/eq8z98n38f8/1/SmfQ6pm8Zz6r+a50bm0iVJIfCocZu+hqT8VKWjg03GHiteDGmhN1lTtQs2sdPZvXB6/RpWMZh8d29dwCSFSStaDJDxeYOO78QIm949tln2blzZ4r2v//+O82exv79+6eowGnrNXR3d1eTJFsxmvj4eCIiIggKCgKSKnlOmDCBwMBAPvvssxTnnz59OteuXWPlypV06tSJgwcPppgnuWrVKiCpB65KlSrodDpWrlyZ6lDWgIAAQkNDNW1lypTRbBsMBkwmE/o05l63a9eOVq1aqQls6dKlUyyJcfr0aapWrUqNGjX4999/AVi0aBH9+vWjb9++zJ49O9XzCyEeP3XqdqLlv9P4zfNBzYlThgt8tH0FE5p2d2JkIjWSHAqHuXjvNjtvL9ckX5mVqNPxTtHCDHW5Q/Pb/7Bwjze96pdGr09/WFhGhFuvauIr7lIcPPwccm4hRN5h6zlr2LAhFStWTHf/3bt3c+HCBbW4TXK2dQr//fdf6taty/PPP6/OA5w/fz6DBg0iKCiI+/fv0759e6Kioli1ahW+vr7qOcxmMxEREURGRvL666+zZcsWbt26RceOHdm+fbs6vzC5jRs3cvbsWV566SUCAwPZuHEjrq6umt5ISOrFTExMxGg08ssvvxAXF0evXr2ApOG0S5cupUiRImkmhpDUs5leIRrbsNX09hNC5B9DarzGtrNfEZPsNWblpU9pWTGY2oHlnRiZsEeSQ+EwozZ+CoY4TVvj2DgMtjULvYtCyTopjguNCeW/e/9p2uYW9KORcS2Xb9Ri25nbvFA1+5WtImIjCTOagAeJZqnijbN9XiFE3mNLXvr165diTpw9ffv25cKFC2kmPUeOHAGShn3a3LhxA4CgoCA6d+7MsWPH0Ov1DBw4EKvVislkIi4ujthY++t/7d27l6FDh7JgwYIUj02bNg2At99+m4sXL/LSSy9RokQJ9Zo2ixcvVn//9ddf8fX1VQvz3L17l6VLl1KsWNZfY998802effZZmjdvTnx8Uu+Am1vq87hPnz7NgQMHpIKpEPlEqbq9GL1/Kh/6J5uXrDczY+9SVnR632lxCfskORQOsfnccS6aNpF8NNaL0THMCLubtGFwhS6bwD/lN0Rmq5np/0znh9M/aNr3FFAoVuorvt3jxQtVsz/0859/V2F5aLhY7bry4UQ8WqxWhfux6S80/jgp6OnqsNEBGZVeL1lW2NYbTL7W4Pnz5wF44okn8PPzY+vWrVitVm7cuIGrqytlypShZMmSBAQEUKRIEfz9/SlUqBB+fn5cu3aN8ePHs3btWt59911Kly6tnnfjxo3s2LGD5s2b8+yzz3LmzBkAdcinPQkJCURERFC2bFm1zVbdNLU1Gf/880969eqFh4cHLi4uGI1GateuzfLly4GkpTtmzpzJzJkzef/992natCmAWmTnYWvWrKFTp064ubnx0ksvUaRIkbRuqRDiceDqRZfyrfn3+gY2ej94jToZsQOr1Zojr8ci6yQ5FNmmKAqTdk1DZ7CqbS6Kwuj74Q92ajDMbmIIYNQbGR88ntLeQUz/ZzrWZJ8RYz1vc8L0EQeuVKFe6aBsxXn+0kbNdrFEKFpCqmWJR8v9WBO1P9yS/o6PkYMTm+Hv7ZyKwQMHDmTgwIEOOdeePXvQ6XQ0aNBAbTt48CCQtOh8w4YNOXnyJPXr16dOnTqUKlUq3Q9F7u7utGvXTpMYXrlyhYEDB6LT6fjkk08A1PUN01rn0DbvMPlwVltymLwtObPZTFRUFIqiYDabCQ8P1wzDXbNmDZA077J3795qtdbUktRmzZpRsGBB7t69y6xZs9SqrUKIx9zTPelz8kdNcmg13mHtf/vpUK1BGgeK3CbJoci2r/ZuINqgrdTXKyKSkuakxZbxDoDGb6Z7nh7VehEUepq3rqwlNtkHJr3rXSbvmclvpT/PepCKwu3o/8D7wYRDf8X+N+VCiPwjs3MOUxMaGsqZM2eoUqWKWoEzKiqKs2fPUrBgQQIDAwHsFqFJy6hRozTbVquVZ599luvXr2M0GunRowfwYK7fhQsXqFChAgkJCSQkJNCwYUPWrl0LoC5/YYsFHiSH9pahAGjdujVxcUnTBWyFZZIvg/Hjjz8CSQV+ypYtq/aeplYNtUCBArzxxhtMmDCBOXPmMHbs2CxVThVC5DGl6vOEdyBBifFcSzY8f/mJdZIcPmIkORTZEpNg4rtTX0KyaTiFLBYGhidb8L75ZNR1LdLRpPFElhz9kWE+RkKTfQN+OWE392OjKeiZshhEhtw+xRVDIsmr0RQs8FTWziWEyFOs1qRRDfaGOmZ2zmFqduzYAaBZemLPnj1YrVbNMNPs0uv19O3blw8++ACz2awOJ7UxmUyaOJOvtXj58mVAW6nUlhzaK7STGtt9PHjwoLo8R//+/QEICwsDSHOJimHDhjFt2jTCw8OZPXs248ePz/C1hRB5lE6H/ukevHToK74t+GCkwrmYXZgtFoyGbFQzFA4lg3xFtoz98zusLtriB8PuR+BtK0JTsh7UeDXjJ3TxoHLtQSy8FYredg5Ap4/ni72/ZDlO6+U9nHHVFpIoH9goy+cTQuQdtgTJz89PbTObzVk618NLN9hs3boVgEaNHryu2Baft83DA/j+++/x9vbO8M/IkSNTXGvkyJEMGTKELVu2cPXqVe7fv8+5c+cAqFatmrqGYEREBKtXr1aPO3XqFADlypVT29LrObTHVjl11qxZQNKQ1E6dOgFw6VLSUkYPL7+RnK+vr5qQz5gxg8jIyFT3FUI8Rp7qRssYbfEtxRDJ8qPbnROPsEt6DkWWXbhzhx1hS9Al+1dUwWSiQ5Ttm2odtJoOaawZZlfdAQTt/oIGcfHs8fRQm/+8+isfkLUCMlcu7ybioW+lmpR+OkvnEiInFfR05eDEZs4OI1cV9Ey5TIMj2ebaJU8ObcMwMzvn0DbE8mF//fUXkNSb1qdPHwwGA+vXrwfgueeeU/dzcXEhJiYmw9ezNyexUKFCzJ07V9NmS/Igac1Cg8GQYvmLo0eThv/XqlUrxXGZSQ5tKlasSLly5WjVqhUeHkmv1ba1I9Mbqjto0CBmzpxJ6dKluXbtWqprNAohHiO+JakY9AwVE/7jXLLXp59Or6dPrRecGJhITpJDkWWjNn6OzhitaXvrXviDf1S1ekGJmpk/sUdBqN2X9ke/0ySHMfoz7L96juBS6c8Peti528cg2bQWo9VA7cByqR8ghJPo9TqnFWd5XF28eBHQ9mYVKlSIypUrp9j31q1bREZGUqhQIbuVNO0Nl1QUhVatWjF79mzmzp3L4cOHGTx4MGfPniUoKIg6dR4s4WNL2J599lm2b9+easxdu3Zl1apVdtc3zAqz2cyePXvQ6/WaeLKTHL777ruMHz9e7Zk9deoUhw8fxs3NjSZNmqR5bOXKldmxY0e6+wkhHjNP96DV1jeYVejBa9u1+L+JTUzA00Xe+x4FkhyKLNl05j8uJf6BLtmX2o1j42gYl7TGFW6+8PykrF+gwTCa7v8GX4tF0+P31T8rCC71XubOFR/BFfNdwE9tKmAtJuPbhcgHLBaLupxE8rl2S5cutbt/3759Wbx4Mf369ctw8RidTsesWbPo2LEjPXr0YO/evWphlm7duqFLNnrCkMnXnczubzab2b9/P0eOHOHo0aNcvXqV3377jU2bNhEZGUmdOnU08wvv378PZC05tMVnq3RqmzvYrl07TdGa1EhiKEQ+VLUNL254k1nJmqyGOBYe3MSw+m2dFpZ4QJJDkSWf/P01Ov2DOTsGRWHMvfsPdnh2LHhnY/0qnxK4PdWF1ld+Z4Xvgw8tx8M3YzJPwDWNcu0p3DzM2YfmGxb0qJr12IQQecbZs2fVhdkrVKigeSwxMZFDhw5pFq2358aNG3h4eKRZZAWSkp3Dhw/TtWtXdQ7iw0lXZtfzejg5PHv2LBcvXiQsLIybN29y7do1Ll26pM45PHPmjKYATu3atQFYtmwZAO3bt9ecLysFaez5+uuvWbduHQaDgUmTtF8MpjZPUwiRD7l4UOqJ9tS4uYFj7g96Cted+12Sw0eEJIciS8JMZ9ElK/zXOSqaconJCjw82TH7F2n4Ou1O/qhJDq3GCBYe2sT/6rXK+HluHOTMQ0OzKhaqkv34hBCPvN27dwNJQ0qTL+EA8PHHHzNt2jSWLFmiFlR52J07d2jWLGkO6J9//kmpUqXSvF7hwoV54okn1OTw3XffxWQyMXnyZABNL2JW/Pnnn3aL1CS//qRJk6hevTqVK1cmICCAGzduqIVpOnTooNk/O8NKIWlI7fTp09Vew/fee48nnkhaP/aXX37h1q1bLF68GABPT88sXUMI8Zh5uicvnVutSQ5DzQcIj4vBz8P+Gqki90i1UpElClbN9lPxyb4Z9q8APqlXqsuwIpWoWu5FqiaYNM0/nv45U6eJufoPV1y034PUC3wy2+EJIR59f/75J6BdYgJg06ZNfPjhh8TFxaW5RMW9e/eIjY3l9OnTNGzYUF3kPTVnzpzh66+/BqBXr17odDqmTJnCm28mrfWa3eSwdevWQFJPX4sWLXj//ff5/fff1WGsxYoVY8SIETRt2pTixYuj0+n49NNPMZlMNGrUiCpVtF+MPZwc3r59O9Vr25bCOHbsmLrfhQsXWL9+PVarlVdeeYUJEyao+//9998MHz6cf/75B5BhpEKI/1eyDs8bi6JLVpXeqk/k239+d2JQwiZXk0Or1Zrp8uEWiyXLJceFk5R24BIRz4ymXZS26E2Y5SBXw8MydryicC70ENbkH8gUeKG8rHEoxOPu/v37/Pbbb4B2OOXevXvp3LkzZrOZIUOG8Pbbb6d6jkqVKrFv3z5q1KjBjRs3aNy4MYcOHbK7r8lkomfPniQmJtK1a1eWLFnCN998A8DMmTNZsWIFSrIPQ1lRrlw5du/ezf3799m4cSPvvfcerVq1omjRonb3P3ToEHPmzAHgnXfeSfG4LTn08kr6tr5Ro0YMHDhQTQQh6b37zTff5IMPPgBg165dVKhQgeHDh3P79m22b9/Ozz//zIoVKzTDZnv16kWXLl2YNm0a//77L23atMnWcxdCPCZ0OkrU6kXdeO2Q842XfnNSQCK5HE8O9+/fT7t27fD398dgMODi4kKFChX45JNPUp2H8Oeff9K8eXN8fX0xGo24ublRvXp15s2bl+031vScPXsWLy8vdDodCxYsSHW/6OhovvjiC5o0aYKfnx+enp7UqlWLqVOnEhUVlaMxPvLKNHbcuUrWoYXfk7gk+/+u6K3M+HtVxo6PvMkFqza5dLMUxN8za0OohBB5x6xZs0hISMDT05NXXnkFSBrq2KxZMyIjI+nevTuzZ8/WHGPvy8jixYuzfft2goODuXv3Ls2aNePIkSMp9hs+fDj//vsvQUFB6nkHDhzI+++/T5cuXejatStWa9Koix07dqDT6VL9WbUq6TXO3nteo0aNMD4079p2XovForZFR0fTp08fzGYzjRo1spucxcYmrTl2/vx5zp49y/nz5/nuu+8096Nnz57MnDmTAgUK8MEHHzB+/HgKFy7MnDlzaNSoEX5+fsyePZs333yTqVOnsnTpUtavX09ERARvvvkmzZo1w2AwcPjwYfbv38/OnTvZvHkzq1ev5vvvv+eLL75Q4xBC5BM1utIyRrs00F3rUUKi7qdygMgtOTrncPHixbz22mtYLBY8PDyoX78+YWFhXLhwgfHjx7Nhwwa2bNmiKdX94Ycf8u677wLg4+NDw4YNuXr1KidOnGDo0KHs2LGDlStX5ki8FouF3r17ExsbS8uWLdVFeh926tQp2rRpoy72q9frURSFw4cPc/jwYRYsWMDvv/9ut0x6vlDGsYvL+9cfzAvb32Sj94Nx6PtvrQWGp3/wjYOcfWi+YQFjeYfGJ4R49Ny8eZPPP/8cgD59+uDt7c369evp2DFpPnSvXr1YuHAher2e69ev891332G1Wtm0aRNAiuSrYMGCbNq0iWbNmnHw4EFOnTrF008/DSQlcKNHj2b+/Pl4eHjw008/4e/vrx47adIkEhMT0ev1avLm4eGR5vzFmzdvEhUVleGRM7bz2vZPSEjglVde4cSJExgMhhRJsE358uW5dOmSZs5lixYt1Mqu48eP54cffgCSitq8/PLLAEyZMoUNGzYwf/58Nm3axPbt29NcmiMtL730EqNGjcrSsUKIPMqnOM8Vrs3HygXM/z+6y6q3Mu/Ar3zwQh8nB5e/5VjP4dGjRxk0aBAWi4U333yT27dvs3fvXs6fP8+8efPQ6XTs2rVLUyp8w4YNvPvuuxgMBqZNm0ZYWBh79uzh8uXLTJw4EYBVq1axYsWKHIn5448/Zv/+/fj5+aXaaxgVFcXLL7/MpUuXKFmyJOvWrSMhIYHIyEgWLlyIn58fFy5c4MUXX8yfPYiFyoFPCcees3Jr2iRoK/bFGENZ/98/6R974yBnHqpUWsanQio7CyEeF/PmzSMyMhKj0cgbb7wBQNu2bRk1ahQTJkxg8eLFaiXQgIAAZs+ezeTJkwkLSxqybkv8kvPx8eH333/nxx9/pHv37kBSUta5c2dmzZqF0Whk1apVKaqf6nQ69UtQkylpDnW9evU4ffp0qj+tWrXS7J8e236JiYlA0tzBYsWKodPpGDNmjN3nAzB16lSqVq2q9lgGBQXx8ccfq4+///77vP7667z66qtqYghJX4q2bt2atWvXcvfuXXbv3s3s2bMZMWIEr7zyCsHBwZQvXx5/f/9012p8uEiOECJ/KFy7z4Ml0P7fziu/OikaYZNjPYdjx47FZDIxduxYpk2bpnls8ODBbNmyhdWrV7Ns2TLGjx+PoijqG/hXX33FkCFD1P0NBgNTpkxh48aN/Pvvvyxbtkx9Y3aUQ4cOMWXKFPX6D1e1s/n222+5cOECLi4ubNiwgSefTCps4u3tTd++fQkMDOTFF1/k6tWrzJ8/X31O+UaZZxx/TqMrDat1o9j1nwhN9m3+wiM/0LZq3TQPtd74J0XPYY2iTzg+RiHEI2XKlCl06NCBnTt3apawsPUmJmc0GmnZsiUHDx7kySefpG3btnTt2tXueYsUKaL2PkLS+9PkyZO5efMmgwYNom3btEux25bVyKi4uLj0d+LB8FDbf4OCglixYgWDBw9Oc6mO2rVrc+rUKaxWq5ogJufp6ckXX3yhGa76MA8PDxo1akSjRqmPGjGbzcTGxhIXF0dcXBwJCQkkJCRgMpny7ygbIfK7yq1ovmkMO5MVMr6rP8fFe6GUK1TMeXHlczmSHMbExODp6UndunVTrHdkU7duXVavXs2NGzcAuHbtGuXKlSMoKIjBgwfbPaZOnTr8+++/6jGOEh8fT69evUhMTKRdu3b07Nkz1X1/+uknIOmbTltimFzz5s2pVq0aJ0+eVKvH5SulcyA5BFzq9OGV/xbxbUFfte1y3HYiE2LxcUulPLrVws2QY0QV1y7G3KRMjRyJUQjxaKlZsyY1a9bM0L5Lly7N8nWeeOIJdu/enaE1DLt160bLli1xc3NLc7+VK1dmagpFcHCw3fmJGa0Qml7sD6+3mFlGoxEfHx98fHyydR4hxGPExZ3nyrfG/d5fxP//a5CiU5h74Bc+azkknYNFTsmRYaVeXl6sWbOGAwcOqBXQHnbz5k3gQfnsUqVK8fvvv7Np06ZUS30/fIyjjB8/nlOnTlG4cGG1slxqzpw5A0DTpk1T3ScoKAiAiIgIh8WYZzh4vqHKvzwtC2h7/BINiczZl8bwgztnOafXztfRWdypWbxsTkQohMjHMrq4vZeXFyVLlqRIkSI5HJEQQjz6fOv0o0msdoTEwRtrnROMAJy0zqHFYmHt2rUA6uLC6YmMjGTz5s2ZOiYjdu7cyRdffAFAy5YtmTNnDiNHjuSjjz7i6NGjKfa3FSnw8PBI9Zy29Z9KlHDw3LtHXcEy4Fsyx05fsf7/qPvQ2PTNF9L4Zt3OfEMPXVCGP8QJIYQQQogcVKImzym+mqY7hpucCLnqpICEUz4lL1y4kKtXr6LT6TJcoWz69OnExcXh4eGR6rDTzDKZTPzvf/9Th+Js376dgwcPcuzYMaZMmcLTTz9N7969iY5+sBSCrYLbwYMH7Z7zzp07nDx5EkhKNjPj+vXraf7cunUrC88yF+XEfMPkKremTZy2VzlMf4HNF1MZvmunUmlxD6lUKoQQQgjxSNDpaFq9J97/vxxPUhvM++dn58WUz+V6cnjr1i11weH+/funWkEtuePHj/Ppp58CSUNAAwICHBLL3LlzOX36NADTpk3jypUr/Pbbb2zfvp3r16/Tvn17li5dyssvv6yuIfXqq68CsGTJEi5cuJDinKNHjyYhIYFixYppFl3OiKCgoDR/6tWrl81n7Eh21pt05PqG9hhdaVa1U4oXkPf+Hk9Egp0hvNf/5exDPYdVClXK2RiFEEIIIUSGedfszvMx2rVOD9/e4KRoRK4mh1arlZ49e3Lv3j3Kli3LjBkz0j0mNjaWrl27YjKZCA4OVhPL7FIUhZkzZwJJayyNHTtWM9ywcOHCrFixgsDAQLZt28bvv/8OwMiRI2nSpAkRERG8+OKL/PHHH9y9e5cDBw7QrVs3li1bBiRVa02v4EBe5kZiysbSOTTfMBmfuq/xWnikpi3KcofJeydrizEkxhFz+xRXH1qrrF5gyiJCQgghhBDCSXyK84JFW6MkyhjCvqtnnBRQ/paryeG7777LX3/9hbu7Oz/++CO+vr7pHjNw4EBOnTqFv78/K1euxMXFJd1jMuL8+fNcu3YNgEGDBtndx93dXR0aun79egDc3NzYtGkTEyZM4N69e7Ru3ZrChQsTHBysVpYrW7Ysw4YNy3RM165dS/PnwIEDWXmqOcIT7dw/s0dh8AvK+Qv7l6e9R7UUcw83XdnEmvNrHjTcOsZ5Fz1KsuJGiqLj+XJSqVQIIYQQ4lHyTGADCj60ZM63h35xUjT5W64lhz/++KO6sO4333xDnTp10j1m+vTprFixAqPRyI8//qjO93ME20LHQJrDNYsVS1pn5erVBxNj3dzc+PDDD7lz5w6nTp1i3759fPLJJ+rjM2bMyFKvYcmSJdP8KV68eKbPmVO8dNrkzOSXe3P5Cj7zPz4Ou4vvQy8iUw9M5WLExaSNGwc589B8Q6OlKH4e9qvnCiGEEEII53AtXZ8XHx5aenebk6LJ33IlOdy3bx99+/YFYNy4cfTu3TvdY9asWcO4ceMAmD17Ns8//7xDY/Lz87P7+8MiI5OGMNpbksNgMFC1alVq167NwoULgaR1DjM71zAv8iBBs23yq5hr19ZXaU0BnQ8f3LmnaY8zx/H2zrcxWUxwI+V8w0IuZXItRiGEEEIIkUFBwbwUrU0OzcZbbD53xDnx5GM5nhyeOnWKtm3bEhcXR5cuXfjoo4/SPWbHjh306NEDq9XK2LFj+d///ufwuMqVK4e7uzuAWpTGnv379wNQtWrVVPf57rvvOHv2LK6ursyePduxgT6idA8VpLF4FMq9ixtdSXiyKy/ExvFqZJTmodP3TvPx/o/ZE/ovRx7qvS3rUyH3YhRCCCGEEBlTtCpP40Yxs3Z96oVHZWhpbsvR5PDkyZO88MIL3Llzh6ZNm7J48eJUF7i32b59O23atCEuLo7u3bszderUHInN3d1d7Y1MPiQ0uQ0bNvDPP/8A0LZtW7v7REdH88EHHwBJRWgqVcqf1TAfThZzWqHGAwEYcy+c8iaT5rHV51Yz2FvhjJt2WGnNYk/kWnxCCCGEECKD9AYMJevQ8qGhpScjdqorBojckWPJYWhoKC+88AIhISFUr16dtWvXpjsP78SJE7Rp04bo6GiaNWvGokWL0k0ms2Py5Mno9Xp+/vlnBg0axMWLSfPV7t27x1dffUWnTp0AaNGiBcHBwXbP8cEHH3Dr1i3KlSvH+PHjcyxW8RD/8twoFIyHojDt9l1creknp03KSjEaIYQQQohHUlAwL0XHaJqsxrus+W+fkwLKn3IsOfzss88IDQ0F4L///qNYsWK4u7vb/dm5cyeQlGjFxCT9o9ixYwcFChRI9ZgrV66o17py5YravnTp0gzHaJsr6Obmxvz58ylfvjxGoxF/f39GjhxJbGwsFStWTPWcp0+fZtasWQDMmTMHDw+PLN0rkTXeDV8DoHJiIm/cv5/2zolFebJoqVyISgghhBBCZFpQME+YEimdqF0ubfnxdU4KKH/KseTwyJEj6u9ms5mEhIRUf2zdxcmPSUxMTPOY5GvaKYqitlseqmCZnt69e3P8+HHGjh1LjRo18PHxwcXFhXLlyjFq1Cj27dtHkSJF7B47YsQIEhMT6dq1q7rkRX6gWU/wQWuux+H7dHsi9X4AdI+MZtS9+xQ1u1MUA0XNZvWnRKwnlXSDNetYCiGEEEKIR0jJOuh0elo+VJjmXOxuTA/NRRQ5J8c+LW/evBlFUTL007RpUwDOnTuX4WOSL2tRpkwZtd1WFTUzKlasyLRp0zh69Cj37t3DZDJx4cIFPv/8cwoVSr3Qiu05/vDDD5m+Zt6nS2MrlxhdCS3fUb3+axFR/Hb1MlsvXWLrtZvqT8VrzQkOrO6MCIUQjzjbnPimTZsyefJkh58/Li6OZs2a8fPPP2N24oebCRMmUKdOHdasWZP+zg4SFxdHhQoVqFChAvfTG93xCPntt9+4e/duuvudO3eOXbt2ERERkaPxbNu2jc6dO3Pp0qUcvY4QTudWAIpVo1WMdmgphkiWH5VlLXKLdKWITLPbcegkJV8YrNn20GmL00QqnuywPkWNkr65GZYQIo8wGo3s2LGDHTt2EB4e7vDzz5kzh61bt9K9e3fOnj3rsPNGR0ezaNGiVEZypHT79m0OHjxIfHx8+jvbkZCQwKeffkpsbGz6O/8/d3d3Ll68yIULF9Tq4LkhJCSErVu3MmPGDDp27MiLL76YqeP79+9PQEAAM2fOTHO/7du306RJE2rWrJnpGLds2ZJmpXQbs9lMv379+Pnnn5kwYUKmryNEnhMUTLlEM5UTtJ/nfj7zm5MCyn8kORSZ9gjlhngEVOKsV+1UH//Y3J0oPHkyUJJDIURKyQulpTVSJCvu3bvHxx9/DCRVs37iCcdVTB4wYAD9+vXjhRde0MzBT40tOXvrrbeoUqWK5qdixYoEBQVRsWLq69W+8847jB07lqpVq7JuXcbm/+h0OlxcktabTa8gXUYkJCRw584dzp49y7Zt21i2bBnTp0/n9ddfp1OnTtSuXRtfX1+KFy9Os2bNGDNmDL/88gubN29m0aJFGbrG+fPnCQsLw2Kx0KZNmzT3tU1jefLJJzP1PAYPHkzz5s3VSudpMRqN6prPK1eu5OTJk5m6lhB5TlBSAciHq5Zejd9HTEKCvSOEgxmdHYDIm1IkiE7sTrTW7AO7D6Zo32etyipLUwp6uhDoJ8WChBApGQwG9feM9G6ZTCZcXV3T3Q9g0qRJ3L9/n/LlyzNx4sQsx2hP79692bVrF9u2beOpp57i66+/pmvXrqnub3ueMTExKYa3ms1mTCZTmsNeCxYsiLu7O1evXqVdu3Z06NCBOXPmEBAQkGactutmZc73tm3b6Nu3L1FRUURFRWV4WK7BYKB48eKULl2akiVLUrJkSRIfKnCRmi1btgDw/PPPp7s0lS05LFUqc8XOfH2Tvqz88ccfee+996hSpUqa+/fr14/333+fkJAQZsyYwffff5+p6wmRp/x/cvhSTAyzCvk9aDfE8v2hjYxo8Ipz4spHJDkUmWZvGFNcooU/jt+iSkAByhXxztV4Kj7blXu7J1KISLUtQXHhncQBKOh5MtA3R5dEEULkXcmTlgkTJvD+++/b3c9W+OzJJ5/UFE9LzZEjR/j6668BmDt3rsOHVbZq1YojR47QuXNnduzYQbdu3bhx4wZvvvlmmsctXLiQdu3aZfp6kyZNonv37gwePJitW7fyyy+/oNfr+emnn5g1axbz5s2ze5xtGGtqCdCAAQMYM2aM3ceioqK4evUqkNTzWLBgQQoUKICPjw8FCxYkICCAEiVKULx4cUqUKEFgYCClS5cmMDAQozFjH29u3bqF2WzG3d0do9HI+vXrAXj55Zc1w4zNZjMxMTGULl1abTP9/xq7RYsWzdC1bN5++23mzZtHVFQUU6dOTbdX09XVlUGDBnHkyBEGDBiQqWsJkef4lQLvAAKjQ3gqPoGj7g9GHWy78rckh7lAkkPhEL8eucGJHYdwMehY+low9cv559q1DS5unCzRmcY3v1PbvjB35LJSHIDqMqRUCJEBJpNJ/cCfHYmJifTv31/tWerQoUO2zrd9+3bq1KmTor1IkSJs2rSJPn36sHHjRrp3756t66SnQoUKbN68mWnTpvHll18yd+5cIOn5njlzJs1jU3s8ebL1MFsl89KlS3P58uWsBZ2OcePGsXjx4hTtr7/+Oq+//rqmzWAwaHovbb2RBQsWzNQ1CxUqxLBhw5g6dSorVqzgww8/pGTJktSqVYuIiAj8/Pzw9fW129s6adKkFG0JCQlERkZy+/ZtLl++7JAhvEI4jU4HQfXgv19pGBevSQ5D4i7n6KXjEhJxMRowGvL3rDtJDkWm2RtAauuXS7QoDFt+iIPvNs/NkCjWZgJL516hrv4Mf1rr8LXlwVwRSQ6FEBnx+eefM2rUKLuP2XqOMlJ189133+Xw4cPqdszDlfcyoU6dOnYTQxtXV1eWL1/Ovn37KF68OB999FGaQ1jbt2+f5vXKly/P+fPnU31cp9PxzjvvMGzYMAoUKACAl5cXAH369EnRC+bt7U1MTIxmxMk///xDvXr1AGjcuHGq18rs0lRZUalSJVq0aIGbmxshISEcOHCAYsWK8cwzz6j7KIpCYmIiMTExREZGYjQacXNzI+H/5z/5+Phk+rojR47kq6++ol+/fmqvclRUFHfu3CEqKopbt26h0+kICQnBarVSokQJdDodFouFkJAQjEYjRYsWRVEUdVhwbGxshgsUCfFIK1Uf/vuVCg99WRelXM+RyyWaLUxc0IpN7jcwKPBqQH/GvvRGjlwrL5DkUDjc3Zjsf/OeWZVK+DOx5BjevXwvxWNSjEYIkV1GoxFfX191vlhqVq1axbRp0wD44osvUvQ+ZURoaCilS5cmISGBkSNHpru/Xq+nYcOGQFIyBtCkSRPKli2r7nPp0iV27txJcHCw3SGeJ0+e5N9//7Vb2fP8+fOYzWbNcbbEEFCLzmTUrl27gKRlpNKas+jIpT8sFgsJCQnExMRo1i4eP34848ePB2DgwIEcOHCASZMmMXToULvn6dq1K6tWrdK09e3bN8UyWtu3b+fZZ59NNZ7ixYtz/fp1/Pz81LZz586l2C8gIIDQ0FDOnDmDt7c3ly9fpmzZslSuXJkTJ06k97SFyJv+f95hhYfnChsiuRoeRik/++uPZ9XGHYv5w+MmoMOsg6W3F3LvDz1TW41y6HXyCkkORabZ/2LS+d9W9qhfigN2ksOSBaUYjRAi5/3222/06tULgCFDhthNDCMjI7l58yaQNEzT3ty4L774goSEBAICAujSpUuKx00mEx9++CGjRo1KUWHVVixnyJAhmgI1K1euZOfOnfTs2ZPhw4enOGe7du34999/ad485aiP4cOH89dffzFy5Ejee+89TWII2qI+GaHT6QgODqZ27dQrTcODYZtXrlxx2LxxNzc3u8t53Lt3jxUrVuDv70/fvn3ZtGkTVquVZ555Rk24ATVmFxcX9u7dy4ULF2jVqpWacP7xxx+EhYWlW6gH0CSGqbHdg8wm4ELkaQE1wOhOUGI8LopCYrK//52XT9Dz6eccermfL/+SIiP6Pew7pjLKodfJK/L3oFrhQAo6fSylS3xNiXJTGPxTynkROa3lkynfjJ8o7iPFaITIh+Lj4x0yfzA1tgI1NgsWLKBDhw4kJibSqlUrvvzyS7vHrV+/nqpVq1K1alXCwsJSPB4ZGakWdxkyZIjdyqgff/wxU6ZMoWrVqvzwww+axzJSiMVqtXL06FFmzZrFyy+/zPbt2/Hy8qJQoUIpersiIyMJDQ0lMTGRGTNmUK1aNf78888U58uM0aNHs2/fPubMmZPmfrb/fzqdDi8vr2z9pJfAfvnll8TGxjJixAg8PT3p1KkTL730Erdu3dLs99Zbb7Fy5UqWLl2qLmHxySefsGjRIhYtWkSxYsUA1P9m1pw5c1iwYIH6b8OWyKZWIffu3bvMnDlThpOKx4vRFUrUwgUo81Dv4eGQ9NcHzRRFIdqcynJA+fTvSnoORaYpKChoEy43zHQI+IBNvknte2LXsOKfYLrXbZ1rcbkZDQx7rjxztl1Q2155ukSuXV8Ih7BaIS5lD/hjzaMQZGGpg7QMGDCA5cuXo9frcXd3z9CXRG+//XaGlpwwm80kJiby3HPPqUsfGI1GjEYjTZo0YfXq1akmacmrlnp4pBzVEB8fz6hRo1i1ahWDBw+2e45BgwZx+PBhfv31V7p3786yZcuYN28epUqVSpEkmEwmzpw5w759+wCYMWMGEydOJCIiQo27bdu2LF++HEVRUtwnHx8fDh48yPfff8+4ceO4du0aLVu25Msvv2TEiBGA/QrWjmDrNatWrRrHjx/P9vksFovdXsPQ0FA+//xz/P39GT16NJA0jzIqKkqdT2lPaGgooO0BDA8PV4cgPywmJoZSpUrh6emJq6srRqMRvV7PiRMn1OT1s88+4/Llyyxfvpxt27YRHx+Pm5ub3X+/N27coGrVqkRFRVGpUqV012UUIk8JqgdX/6aCKZFzyb4cOX8/9TnRWbFgzducTaWY9IYT+3mpen2HXi8vkORQOEQf45+M9y2sadtzdn2uJocAI56vyNnQaHaeDaNxxSL0aVgmV68vRLbF3YNPyzs7itz11gXwKpz+fllgtVqJjY1Nf0eyV620b9++lClThnr16qW5bEXyhNBecli0aFHef//9VJfUAChRogTr1q1j6dKljBw5kj/++IP//e9/bNiwQS3iEhYWRoUKFbh8+bKmsEtiYiKtW7cmODiYunXr8vTTT6txpJZA6/V6BgwYwEsvvUTXrl0JCQmhT58+6uO2HtTFixfbrfyZ2rlPnDhBtWrVUn2etkI+yecIZofBYEiR7MXFxdG7d28iIyOZOXOmWlzGltyn1RNrSw6TJ4Lh4eH4+NgfsaIoClFRUUDSPQsLC8Pb21tNDA8fPqxWZe3Tpw+xsbFYrdZUE9TAwECCg4PZsmULU6dOleRQPF5K1Yc9UMGk7TkMib/s0Mu0bfY2W5f/yQnPlCMg3tn/FvXKbsbf27FLET3qZFipyDR7XxLP90v5LemN+MsM/vUz9l1Nu8S5I7m7GJjfuw7/TW7Jgj51cHfJ3FwYIcTjYc6cOURHR2MymVAUJdWf5IujT5kyJc19FUXBarWSkJDA7du3mT9/vuaaTZs2xdPTU93et29fiuqmyYcHPjzUMbMFWHr16sXRo0d55ZVX+O677zTnKFq0KB4eHtSuXZtRo0YxZMgQAN555x2WL1/OyJEjadCgAR4eHsTHx/Pss8+yadOmNK8XGBjItm3b2LBhg6ZCpy059Pf3V+faFSxYkMqVK6f4sQ23DAoKSjMxBNREKrPrCGbGoEGD1Oc9e/ZsqlSpQpUqVQgJCQGgQYMGlC5dmoCAAAoXfvAFhtVq5fr163h5eanJocViITo6OsWcTBtvb29MJhN3797lwIEDgHYZjB9//FHdr3Pnzuo6i2lVQ/3ggw8A2LNnDzt37szKLRDi0VQyqaLxw0VpYpXrmR7KnpZiPv5M7/wXne+lPKfVJZztF3PvM+yjQpJD4RCXXFNOlr9gvMGe+4sZvPVVjty6nKvx6PUyz1CI/MzX1xcvLy+HF/LQ6XS4urpSpEgRTTXQh8XExNClSxcaN26coSGRu3fvplKlSvz333+ZiqdUqVKsXbuWEiWShtDHxcUBScNXjx07xv79+/n888/VuYTHjx/XJKFWq5W5c+eyc+dOtWpnWoxGI+XKldN8OLMlMWPGjGHNmjUA1KhRg9OnT6f4sVVDfemll9K9lm3eXU4mh6+99pr6+8WLFzlz5gxnzpxR79HFixe5evUqoaGh6vOEpCGdiYmJlClTRm2zPZ68gE16bL3M8fHxLFiwAIBXX30VLy8v9fk/XHQouYYNG9KkSRMAPvzwwwxfV4hHnpc/+FdI0XOIIZZzd0Mceqmgwv7067WPtddvpnjMbHVc1eS8QpJDkeMseitrD//i7DCEECJdERERjB49WpMIZMW7777L1atXOXnyZLo9OgkJCXTr1o1Lly7Rtm1b7t3L3JzTefPmqXPpbP/18PDQDG0sV64cAN9++y0uLi7odDp0Oh0Gg4E333wTSCq2khELFy6kd+/eaoKYPImpV68e/v7+7Nq1K0Uxl6ioKH75Jem9IPmw1NTYjrf1Rp45c0aNO7M/ydctTK5p06aMGTOGn3/+mXPnznH37l3i4+MpXbo0ALdv38ZkMhEVFaUOIwU4deoU8OC+woPkMLWeQ3tsPckrVqzgzp07QNJ8WUhafgSSlr1Iy5gxYwDYvHkze/bsyfC1hXjkBdUn0GzG/aGewj1XHL+MS1DhApR+qneKdn0+LGoocw5Frijl7dg1aYR4bHkUSpqDl594pN4zktvGjh3Lt99+y8qVK5k9ezYdO3bM9Dk2b97MrFmzgKRhi8OGDUtzfzc3N3766Seee+45Lly4QKdOnfjzzz8z1Ot59OhRhg4dysqVK9mxY4eaUPn7+2v2q1u3LosXL2bXrl2aobS2SqD16tWzu2zGw2JjY5k0aRI3b96kSpUqTJw4kYsXLwJJiZJer6dDhw7Mnz+fBQsW8O6776rHfvPNN8TExFC9enV1Xca02Nb9q1SpEoBmyG7lypXTPR6Skv2QkBC78zttPv3001Qf0+l0uLi44OLioukRPHr0KAC1atVS27KSHNr4+PjQoEEDoqKiaNCgAYD6pULFihXTPLZ169YEBgZitVrV4bBCPBaC6mE4soyyiWb+c3swJP9w6Gkg5Zqs2b9eMNzdomky5L/cUJJDkTv6Nuru7BCEyBv0+hwrziLS9/zzz/Prr78SEhJCp06daN++PXPnzs3QunUAN2/epEePHlitVurXr89XX32VoePq16/Pt99+S+/evdm2bRvDhw/nm2++Sfc421DEV155BUhaExCgZMmSKfbt3bs3DRs2ZPLkyQwZMkRNQjJj3Lhx3Lx5E39/f/r374/ZbFbnz9nmEPbv35/58+czb948xowZg4eHB9HR0cyYMQOAkSNHpnsds9nM2bNnAahSpQqgna95+nTGytkvWrSIfv36pboURFbt2LEDgODgYLUtO8lhp06d6NSpk9prbDabWbVqFYDdtSeT0+v1rF69mqeffho3N7dMX1uIR1ZQ0t9XRZNJkxxejMihL1BL1oMj2ib32Ft2d32cybBSkWnGTM7na2Uqij6TiyQLIYQzdOnShZMnT9KhQwcA1qxZQ/Xq1dW5dGmJj4+nY8eOhIWFUaxYMX7++edMJSW9evVi1KhRQNLwz9TWSrSJjo5myZIleHp60r9/fyBp6KW3tzdFixZV5x/GxcVx4sQJLl++THh4OMuXL6dnz55ER0ercU+ePJmxY8emeb2dO3eqye63335LiRIl2LRpE+Hh4VSqVEkd/li/fn2Cg4O5desWM2fOBGDy5MmEhIRQqVIl+vbtm+69OHLkCAkJCXh5eanJYXprFaYlI+s/2vPff/+xfPlyxo4dS4sWLTh37hzh4eFs3boVFxcXTYJtKz6UleTQxja/cPbs2dy8eZMSJUrQokWLdI8LDg6WxFA8fgpXAnc/yj9UlOZ2fCrrEmaXX1CKJldLdM5c6xEmPYci04yGzH2nMKnb6hyKRAghHK9QoUKsXr2ar7/+mtdff507d+7QoUMH+vXrx6xZs+x++LdYLHTv3p19+/bh6urK6tWrCQwMzPS1p0+fzoEDB/j777954403qFy5cqrJwffff09kZCSvvfYafn5+XLlyhYsXL9K4cWMSExMpXbo0/fv3p2vXrtSsWZNWrVrx+++/M2LECGbNmsXo0aPViqsrV67kv//+o2nTprRq1SrFtcLDw+nbty+KotCrVy81ebb1XLZt21az/6RJk2jdujVTpkzB19dX7TWcOnVqhhK1bdu2AdCoUSN1f3021sK0t7TE7du3OXbsGHfu3CEkJIRr165x+fJlLl26xPXr1wHUYi82AQEB/PDDDyQkJNC8efMUaxxC5grS2PPPP/+ow3Hfe+89TVJsqwwrRL6g10NQPSpc187bjucGVqs1W68JdtmbX6jPf6lS/nvGwkEy1nv4XLw3Xt5+ORuKEELkgMGDB1OrVi06dOjAjRs3WLhwIZUrV+btt9/W7Gc2m+nZs6fauzh//nwaNWqUpWu6uLiwatUqatasyZ07d+jSpQt///03TzzxhGa/2NhYPvnkEwB1TuPmzZuBpF6kzZs3ExYWxt27d9V18mxz9j744AOWLVvGb7/9pvZOLVq0iIYNG9K/f39OnjypmbOoKAp9+/bl0qVLlChRQp1PefbsWdatWwdoq34CtGrVipYtW7Jx40ZGjBgBJPWMtm/fPkP3wVa45rnnnlPbUluHMatCQ0PTHbI5adIkgoODqVKlCqVKlcJgMKi9p7YE2SY7w0ptNmzYQK9evYiOjqZZs2ZqcZp9+/Zx6NAhteBM8vmXQjzWgupR4dJWbZshnhO3r1IjoIxjr2VnrTa9wbEVr/MCGVYqssSaweRwdIt5ORyJEEJknZLsw4Bi54NBvXr12L9/PzVr1uT1119PkRgCvPnmm+r8sMmTJ9O7d8qKd5lRsmRJFi9ejE6nIyIigpdfflmtQmoze/ZsQkJCeOGFF6hZsyYAS5YsAaBNmzYsXboUgJ49e6rLJdiGHfr6+rJ69WrOnTunLoFRr149Ro0aRWhoqJrM2YwfP15NAr/++mt1bb5Ro0ZhtVpp06YNVatWTfE83njjDfV3o9HIxIkTM/T8Dx06xL59+wCyVBDIHnv/b6tXr06pUqUwGo3Ur1+f0aNHs3LlSs6dO0epUqUAGDFiBK1ataJcuXIYjUbWrVvHiRMn8PLyolu3bprzPZwc3r59O9V4bIvdh4WFcezYMSBpeO/KlSu5f/8+5cqV44cfflB7Ru7cucOwYcNYsWIFkLJHU4jHVlB9ipsteD5csfTqKYdfKjHRlKJNZ8h//Wj57xkLh7CiB9JfhDTk6H7Klnk6x+MRQoisSJ40WCwWu/sEBgaya9euVOd0ffzxxxw/fpwqVapoqnPaY5vnp9fr05xD16pVK9544w0WL17Mt99+qyZ4kNRjN3nyZAAmTJgAJM3R27VrF0WKFKFSpUqsWbOGcuXK0aRJE27cuAFo593Z1j20WCz06dOH8ePH88EHH7By5UquX79OXFwcHh4ezJo1i6lTpwLQrVs3dfjokiVL2LBhAzqdTl2IPbk1a9YwcOBAddtsNlO/fn3ee+89Bg0alGb10HfeeQeAOnXqaCp1Jv9/ZZuHmJ6IiAj1edqzZs0aKlSokGKheXvJZFRUlDondPDgwfj6+moetyWHtp7asWPHcuvWLSZMmKBJ5lauXEm/fv2ApASyZs2atG/fnp49ezJv3jyGDh2Kn58fhQs/KEzVokULOnbsSNWqVWnUqFG6PZ5CPDYCa6HTGahgSuSY+4PX4GOhp4GUQ+CzY83Byyna9Pr813MoyaHIEovOQIaSw3MfcPduR/z9pfqiEOLRk3wxd5Mp5bfGNrYP/Kk99ssvv6RIMGzi4+PR6XSEhYWxePFiIGntuvSGSX7yyScMGzaMsmXLqm2JiYn06NGDmJgYGjdurA67fO+994AHlUJNJhN9+/bVXOPs2bNYLBZNUrp27VqWL1/Orl27uHLlCn/99RcVK1ZUj2vVqhWffPIJZrNZHU66a9cuBg0aBEDfvn01yzmcPHmScePGsX79egC6d+/OxIkTGTp0KNu3b2fUqFF89NFHdO3alVdffZX69etrktbPPvtMHR47fPhwzf1I/v/qzJkzad67h6X2/zZ57PaulTypHDFiBFeuXMHX11dNYJOLjY0F4OLFi8TExLB7924uXLhAjx491ORwzpw5jBgxAkVRGDx4MGXKlGHt2rWsXr2a1atXYzQaqVGjBjVq1KBMmTIEBQVRsGBBfH19GTt2LAaDAYPBwPHjxzGZTJhMJhISEoiOjiYiIoKIiAhefPHFDC/1IcQjz9ULiteggumKJjm8lAMVS7f/dxMeygV1WSxmlZflv2csHELJ4IjkSUX8ab6qFzOHbsjhiIQQIvPMZrP6e3aKfSQvTPKwBg0acOTIEU1bu3bt0j2ni4uLJjGEpHl3o0aNYvTo0bz//vsALF26lF9//RWj0cjgwYMJCwtj7dq1au+ULba9e/dSvHhxtSJmYmKiutD6kCFDgAdrCtpUrFiRzZs3899//1GkSNJ6tXq9npYtW3L48GFmzJiBoijs2LGDGTNm8Pvvv6MoCmXKlOGrr76iTZs2APz1118sXryYiRMncuPGDb766iu++uorPD09+d///qdWNa1Tpw69e/fm6NGj9OzZUxNL8kTNXs+ePbalLGw9thllu5bt38eECRPUxH7KlCmaXj2b8uXLA/Dll1+qlWYLFSpEp06dAPjjjz/UhPeNN95Qi/S8/fbbHDlyhHnz5vHLL79w6NAhDh06lKl4bWxLjAjxWHmqG+X3TNE0xcefdPhlQmM2gJ+2LT9W25fkUGSJEXcgYx+ktnpe41ZkOMV9/HI0JiGEyKzkPUqZTSAyaujQoWpPm7u7Oz179lSHamaW0WikR48eNG/enKJFiwJQoUIFKlWqxPPPP0+ZMmUoU6YMBw8eVHv/vL29GT16NPPmzSMsLIywsDD1fG5ubnTo0IHRo0enes3q1atTvXp1dbtRo0Y0atSI8PBwdu/ezbBhw7h69SqQNF9y3LhxDBgwQLOMh06no2/fvnTr1o3vv/+ezz//nHPnzlGkSBEmTZqk7te0aVOaNm1KYmJiimG3afXspufOnTuZ2t92rcTEREwmE1arFU9PT5566im1ANDDhgwZwrZt29i5cyeJiYn4+fkxffp0tXhMq1at+Oabb/jyyy/58MMPNcc+/fTTfPPNN8ybN48TJ05w6NAhTp06xZUrV7h16xZhYWHcv3+fyMhIdYkSe1q2bJlmL7cQeVLtfpTap10zNsZwB7MpDqNr6kPUM6uAIeX6qcX9Uq4Z+7jTKRn9+k043fXr1wkKSlqD5dq1a3YXOc4t7679jLURizO0r6sVtnbdi59H9sp7C5HXnTt3DrPZjNFo1MylEs5z48YN9bW0c+fO/Pjjjw6/RkhICIsWLaJmzZo888wzOfLh/d69e5jNZjVhzE0//fQTCxcuZODAgbRt2zbDawru3LkTg8GQ4cquZ8+eVYdL5vRHFw8PD+Lj4zl58qRaKfbChQvodDrKlSuX5rGKoqAoSqpl9h8e2ptZVquV2NhY4uLiiIuLIyEhQf0JCAjI9BIq8rok8oLzfy+g/blZmraVxbtQ7cWMFbrKiNeXdOYvRZsgHu9z3GHnzwk5kRtIz6HIkintxnBu5lZO+l9Pd99uAf0lMRRCPJICAwNzPNEICAiwO0fNkWxDRZ2hc+fOdO7cOdPHZbbiZqVKlXL8/5WNvd4527DR9Oh0ujTnk2YnMYSkYb3e3t7ZXk9RiLykXHA/vE7PIibZn8+tEwup1ng4ePg55Bp67yIQ9SA5fCUqGkyx4Jq/lo6RpSxElpwMvcz5gtfS3a+P2zOMeSn14UpCCCGEEEKkRW8w4K4U17Rd1plg9+cOu0aCq/ZLNh3A3XMOO39eIcmhyJLdV06RoE+70l7T0CDe6Dw7lyISQgghhBCPK2+PJzTbF1xdYN88CE+/syIjrHo7Ayrjwh1y7rxEkkORJZ2qNcbDnHY3e/Wqb+fLKk9CCCGEEMKxyvtV0Gyfd3EBSwJs+8gh57c7bD0hyiHnzkskORRZ4u9VgNUdf6dT0BiedW9md5/nSrnabRdCCCGEECIzaharotm+6OKCBeDoSrh1LNvntzulWZJDITIuyK8w7z3fh49av2v38Zg7V3M5IiGEEEII8ThqXOZJzbZJr+Oa0QgosPndVLK7jLN7tCSHQmSer7f9KnnxYZdzNxAhhBBCCPFYKu8fABZtld7zri5Jv1zcDue3Zu8CdoeVRmbvnHmQJIcixyjhV5wdghBCCCGEeEx46bTreKrJIST1HlotWT631V6jKTrL58urJDkUDhGYmPKP0SXaMdWjhBBCCCGECPAoq9k+75IsObx9Co6syPK5Zc5hEkkOhUNUsRZO0aaPk55DIYQQQgjhGBX8ymu2/3P10u6w7SMwxWTx7FKtFCQ5FA5S1b9uijY3w00nRCKEEEIIIR5HtYtX1Wxfc9GRmLwh6hbsnZulc1ul5xCQ5FA4SMXSz6Rou5h8HLgQQgghhBDZ0LhMdc22olM4XbK+dqc9X0D07cyf3F5yGB+R+fPkcZIcCod4qlLK5HC/u7sTIhFCCCGEEI+jkr6F0Fl8NW2bSzQHdA8aTNGw9YNML21hd++oW5mOMa+T5FA4hL9PkRRt+zwkORRCCCGEEI7jrSup2T4UFwNPddPudHgZ/DIIEuMyfF7FXjIZeSvb6yfmNZIcihwTajQ6OwQhhEjX33//zeDBgxk8eDAREY/nEKIJEyZQp04d1qxZk2vXjIuLo0KFClSoUIH79+/n2nWz67fffuPu3bvp7nfu3Dl27dqV4/9mtm3bRufOnbl06VKOXkeIvKK4ZxnN9rXoi/D8RDA+1Clx/Ef4vgWEZ6x6vt2lLMxxEB+elTDzLEkOhcPo89k3K0KIx8O5c+f45ptv+OabbzCbzQ4557fffkvLli35448/sFjsr7v1999/07hxY9q1a8evv/6aqfNHR0ezaNEi+99023H79m0OHjxIfHx8pq5jk5CQwKeffkpsbGyGj3F3d+fixYtcuHAB91ycZhASEsLWrVuZMWMGHTt25MUXX8zU8f379ycgIICZM2emud/27dtp0qQJNWvWzHSMW7Zs4fTp0+nuZzab6devHz///DMTJkzI9HWEeBxVLlhRs33Pcpq1N67BS9NT7nzrKMx/Dq78nf6JU3s5jcxfQ0slORQOE2znQ4fVavd7GCGEeGS4JFsny83NzSHnXLp0KX/++Sevvvoqly9ftrtPZGQku3fvZt26dQQEBGTq/AMGDKBfv3688MILXLmS/rJBtuTsrbfeokqVKpqfihUrEhQURMWKFVM9/p133mHs2LFUrVqVdevWZShGnU6n3ltH3NeEhATu3LnD2bNn2bZtG8uWLWP69Om8/vrrdOrUidq1a+Pr60vx4sVp1qwZY8aM4ZdffmHz5s0sWrQoQ9c4f/48YWFhWCwW2rRpk+a+tqT/ySefzNTzGDx4MM2bN+eDDz5Id1+j0ci4ceMAWLlyJSdPnszUtYR4HNUJfELbYIhl4v7BvHE7FmuX5eBaQPt4TBgsbgv/fJfmeZXUssOo/FV9X5JD4TD141Imh9fvXndCJEIIkXHJk0NXV9dsn+/y5cvs2bMHgDfffJOiRYsSHR1NYmJiqsc88YT2w47VasVkMnH//n1MJlOK/Xv37k2JEiXYtm0bTz31FCtXrkwzJoPBAEBMTAzh4eGan/v37xMREUF0dHSqxxcsWBB3d3euXr1Ku3bt6NixIyEhIWleM/l19frMf9zYtm0bpUuXplChQri4uODu7k6RIkWoXLkyzz//PL169eLtt9/myy+/ZPXq1Rw6dIjIyEgMBgMlS5akUaNGdOnShTfffDPNe5/cli1bAHj++eepVKlSmvvaksNSpUpl6nn5+iYV0/jxxx8z1HvYr18/AgICUBSFGTNmZOpaQjyO2lSui8Gs/UJNp7ew+fZcWvy7njs91kEh7XqIWM3w+xuw/nUwp3xNhTSmFuaznkOZFCYcpoD+SeCGpu3Qf1spVaSfcwISQogMSJ4c2pKZ7Fi4cCGKolCgQAFGjx7NuHHjmDNnTprHFChQINXHtm3bRtOmTTVtrVq14siRI3Tu3JkdO3bQrVs3bty4wZtvvplubO3atcvoU1FNmjSJ7t27M3jwYLZu3covv/yCXq/np59+YtasWcybN8/ucbZhrFWqVLH7+IABAxgzZozdx6Kiorh69SqQ1PNYsGBBChQogI+PDwULFiQgIIASJUpQvHhxSpQoQWBgIKVLlyYwMBBjBue837p1C7PZjLu7O0ajkfXr1wPw8ssvEx4eru5nNpuJiYmhdOnSapstaS9atGiGrmXz9ttvM2/ePKKiopg6dWq6vZqurq4MGjSII0eOMGDAgExdS4jHkavRyPI239Pv9xHEGS5oHgux/k3zLRf54sXZPPvvDDi/RXvwwUVw+zS8ugQKFNM8lHpymL96DiU5FA7jX/ZluK/9gLD//O+0ayLJoRDi0ZU8OcxKD1dyd+/e5YsvvgBg4MCB+Pn54eXlRWBgIG5ubhgMBvUaERERhISEYDQaKV9e+y232WwmISGB+Ph4TXzJFSlShE2bNtGnTx82btxI9+7dsxV7eipUqMDmzZuZNm0aX375JXPnJi00nZiYyJkzZ9I8NrXHkydbD7NNSyhdunSqQ3Oza9y4cSxevDhF++uvv87rr7+uaTMYDJo5qbbeyIIFC2bqmoUKFWLYsGFMnTqVFStW8OGHH1KyZElq1apFREQEfn5++Pr62v23OGnSpBRtCQkJREZGcvv2bS5fvuywodFCPMqqFQtie69V9F7zPmfiftM8ZjaGMGzvCF4t/TqTij2ZtO5hctf2wbdNoesyCKytNsuw0iSSHAqHKVqrG2zVJofnTeedFI0QQiQVF/n777QLEcTExKi/V61aNd1zTp06NdXet48//pjIyEj0ej3Dhg0DYNq0aUybNi3Fvp999hlvvfUWVatW5dixY+le1x5XV1eWL1/Ovn37KF68OB999BETJ05Mdf/27duneb7y5ctz/nzqr9s6nY533nmHYcOGqb2dXl5eAPTp0ydFL5i3tzcxMTGawjn//PMP9erVA6Bx48apXiu1Qj6OVKlSJVq0aIGbmxshISEcOHCAYsWK8cwzD9buVRSFxMREYmJiiIyMxGg04ubmRkJCAgA+Pj6Zvu7IkSP56quv6NevnzofNCoqijt37hAVFcWtW7fQ6XSEhIRgtVopUaIEOp0Oi8WifqFQtGhRFEXBbDZjMpmIjY3NcIEiIR4Hni5u/PzqJ3y6qxZLzk8H/YPpTTq9iZ+ufcq/rs354ZWv8fp9dFLlUZuom7CgGVR9GRq9DoG1ZFjp/5PkUDhMcT/PFG2nPXL+zV0IIVJz586ddHu1ksvIvvbmAAJcuXJFHT5apEgRypUrl+Z5bHP2SpQokeH47NHr9TRs2BBISsYAmjRpQtmyZdV9Ll26xM6dOwkODrY7xPPkyZP8+++/dit7nj9/HrPZrDku+TDY1Ho2U7Nr1y4AKlasmGYhHkdVjoWkRDMhIYGYmBiKFHmwLu/48eMZP348kNTTe+DAASZNmsTQoUPtnqdr166sWrVK09a3b1/69u2radu+fTvPPvtsqvEUL16c69ev4+fnp7adO3cuxX4BAQGEhoZy5swZvL29uXz5MmXLlqVy5cqcOHEivactRL7wVuPONCpVneFbR5No0Na6uGTazLOHz7Kg7fc8/dfbEHH1wYOKFU6tTfop2wQfg7/9C0jPoRBZ4+eZ/UIOQgjhSLYCMz169GDZsmV299m4cSMvvfQSkMoiyP9Pp9NpzvmwwYMHq71JD0tMTEz1uD///FM9d3L//fef3UTOZDLx4YcfMmrUKAoVKqR5zHaNIUOG0LVrV7V95cqV7Ny5k549ezJ8+PAU52zXrh3//vsvzZs3T/HY8OHD+euvvxg5ciTvvfdeivmRmZ2nqdPpCA4Opnbt2mnuZxu2eeXKFbv3Jyvc3NzsLudx7949VqxYgb+/P3379mXTpk1YrVaeeeYZNeEG1JhdXFzYu3cvFy5coFWrVmrC+ccffxAWFpah6rPJE8PU2O5BZhNwIfKbhqWr8Fe3n+n2yztcN2/XPJZguEKvg+8zpPY7DL34A1zelfIEl3ZSuZAf+3xTjgSwRNwk+7PR8w5JDoXDGPQ6iiVaCXWRIrhCiEdDRguTZPec3377LRs3bkz1GBcXF3Uo4vDhw9WKlfbMnj2biIiIVIvUfPzxx0yZMoVvvvmGL774gm7duqUZ28OsVivHjx9n+/btbN26lTfeeAMvLy8KFSqUorcrMjKS0NBQEhMTmTFjBj/++CPz58+nRYsWmvNlxujRoxk9enS6+9l6aHU6HZ6eKUemZEZ8fHyaw1S//PJLYmNjGTt2LJ6ennTq1ImoqCjOnj2rWeLjrbfeUn9v164dFy5c4JNPPqFGjRoAVK9enbCwMIoVK5biGhkxZ84c3NzceOWVVyhSpIiayKb2xcLdu3dZvHgxo0ePdlgCLURe5efhxYYeXzF+83f8en02On2y0QeGOOZdmsq3lsJUCHya2vE3KG+Ko0xiImUSzRRJ4/XBEHeXuTMmUqBuD1rWLEeRAo/3vF5JDoVDFY/1I9Q3UtNmtVqzXeRBiPzCqlgJTwh3dhi5ys/ND70uZ14jcuK15+EE7OTJk2qyU6NGjVTnDxqNRhISEnjzzTcpU6ZMqudftmwZERERqSZ6gwYN4vDhw/z66690796dZcuWMW/ePEqVKpWi59NkMnHmzBn27dsHwIwZM5g4cSIRERFqTG3btmX58uUoipIiwfDx8eHgwYN8//33jBs3jmvXrtGyZUu+/PJLRowYAaTd25odtl6zatWqcfz48Wyfz2Kx2O01DA0N5fPPP8ff31/9/+jl5UVUVJQ6n9Ke0NBQQNsDGB4ejtFotJv8x8TEUKpUKTw9PXF1dcVoNKLX6zlx4oTa+/rZZ59x+fJlli9fzrZt24iPj8fNzc1u4nfjxg2qVq1KVFQUlSpVSnddRiHyi4+bv0aTszV5e9cYrMYwzWMW4x3OAGdcPQAPtd3TaiU2jfeLoVFfwV9f8dvm+qwr9Q4talWk5ZMBeLs9fqnU4/eMhFPFxVQG3380beevnaBS6RpOikiIvCU8IZxnV6U+V+lxtKPLDgq5F0p/xyzI6d4Uk8nEK6+8QmxsLP7+/nz55Zcplp2wsSV7Y8aM0QxVfNidO3eA1IdrlihRgnXr1rF06VJGjhzJH3/8wf/+9z82bNig9o6FhYVRoUIFLl++rOkxS0xMpHXr1gQHB1O3bl2efvppPDySPiCldq/0ej0DBgzgpZdeomvXroSEhNCnTx/1cdtQ2sWLF9ut/JnauU+cOEG1atVSvQ+2QkHJ5whmh8FgSJHsxcXF0bt3byIjI5k5c6ZaXMb2/yqtnlhbcpg8EQwPD8fHx8fu81UUhaioKCDpnoWFheHt7a3+fz58+LBalbVPnz7ExsZitVpTTVADAwMJDg5my5YtTJ06VZJDIZJpWakWNQJ+pvuaN7nLv+nun1ZimFwbwz5KXxvFyxc+ZPpGD1YMrE+Foqm/nudFkhwKhzob/QygTQ63HlxKpdKfOicgIUS+ltPJoaurK++88w6DBg3i+++/z9BwwtWrVzvk2r169eLZZ59l5MiR6rIStiIuRYsWxcPDg9q1a9OwYUMSEhKYN28e77zzToo5h/Hx8bRo0YIJEybYLUhjExgYyLZt27h8+bKmQqctOfT398fFxYWQkBAKFixod/2/8PBwQkNDCQoKSjMxBNREKrPrCGbGoEGD2LRpE5A0nPebb74BHhQLatCggbqsiNlsVhN3q9XK9evX8fLyUpNDi8VCdHR0qstzeHt7q0NlbYVlki+D8eOPP6r7de7cWV1nMa1qqB988AFbtmxhz5497Ny5kyZNmmT1Vgjx2CnhU4i/en3HyD++YsftZWBIOXIgLe6pjIqorr9MOd0tLkQFsmTvZSa/8qQjwn1kyFg/4VB9nm2Uom1vxAU7ewohRM7LjdL+AwYMYNu2bbz88ssZ2v/SpUsoipLqT1pr/z2sVKlSrF27Vq14GheXVKrd3d2dY8eOsX//fj7//HN1LuHx48c1VUCtVitz585l586datXOtBiNRsqVK6eZZ2hLYsaMGcOaNWuApOG1p0+fTvFjSz5tBYDSEhaWNBwsJ5PD1157Tf394sWLnDlzhjNnzqj36OLFi1y9epXQ0FD1eULSkM7ExETN8GDb42n1Cj/MtoxFfHw8CxYsAODVV1/Fy8tLff4PFx1KrmHDhmpC+OGHH2b4ukLkF3q9ntltXmdn1228X+cbOga9RQ3vjhTW1cXFUhLFar/Yk16B5jGxdh+7p3hzTUl6XbI+hsvHSM+hcKj+jcqy6Gdt23HrHecEI4TI92xDKtevX2+38ido1zlMbR9750wurWULctq8efPU9fJsc+o8PDw0vaa2ZTW+/fZbvv32W7vnSV5sJS0LFy5k27ZtLFmyBL1er0li6tWrh7+/P7t27eLWrVsUL15cPS4qKopffvkFQDMsNTW3biWtLWar/HnmzJkM/f+xp1GjRuzevTtFe9OmTRkzZgz169fnqaeeolChQnh5eVG5cmWuXLnC7du38fPzIyEhQVOJ9tSpUwCa5UpsyWFqhYTssRWaWbFihdorOWDAACDpSwRAcw/tGTNmDDt37mTz5s3s2bOHRo1SfkkrRH5X0NObjtUa0rFaQ0272WLhxO1r/HP9DCfvnOfivZvcuGfi/t1yvJngxhO6KwTo7vOM/jiBurv8Y63EEsuLmEhKKp8skXpxsbxKkkPhUAW9UlZUK+Ra2QmRCJE3+bn5saPLDmeHkav83Pxy7Ny2RC4yMpLIyMh09s7YOofZXX8v+fqD2XX06FGGDh3KypUr2bFjh5pQ+ftr1+uqW7cuixcvZteuXWqhF0gaduvl5UW9evXo0qVLuteLjY1l0qRJ3Lx5kypVqjBx4kQuXrwIJCVKer2eDh06MH/+fBYsWMC7776rHvvNN98QExND9erV1XUZ02Jb969SpUoAmoqllStn7H0lIiKCkJAQdV6lPZ9+mvq0B51Oh4uLCy4uLpoewaNHjwJQq1YttS0ryaGNj48PDRo0ICoqigYNGgCwc+dOAE21VHtat25NYGAgVqtVHQ4rhMgYo8HA08XL8HTxMkBSFWZFUbhyN5a9F+/y94W7/H7hDoujW6Q4VqeD+uVSWRsxD5PkUDjc8KrTmP3f2wAoio6+T3ZL5wghhI1ep8+x4iz5ka23x5HrHNrmjWVVx44d0xx6+PPPP2t6M9NiG4r4yiuvAElrAgKULFkyxb69e/emYcOGTJ48mSFDhqhJSGaMGzeOmzdv4u/vT//+/TGbzRw4cABAnUPYv39/5s+fz7x58xgzZgweHh5ER0czY8YMAEaOHJnudcxmM2fPngUe9OYmX87h9OnTGYp30aJF9OvXL9WlILJqx46kL3CCg4PVtuwkh506daJTp07cu3cPSHr+q1atArC79mRyer2e1atX8/TTT+Pm9niX2BciN+h0OsoU9qJMYS+61UuqAn3udjR7L9zl7wt3OHDpHokWhZEvVKBM4dQrGudVkhwKhxtYpyUWxcrWy7t5sWwTetV83tkhCSHyqfLly9OoUaMM9zSlxTZcLysf/pP77LPP0lzKYvv27RlKDqOjo1myZAmenp70798fSOr59Pb2pmjRosTFxeHh4UFcXBwXLlzA29ub8PBwli9fzp49ezh69Cje3t7Ex8czffp0oqOjmT59eqrX27lzJ1999RWQNDy1RIkS/PHHH4SHh1OpUiV1+GP9+vUJDg5m//79zJw5kwkTJjB58mRCQkKoVKkSffv2Tfe5HTlyhISEBLy8vNTkMLXqrRmR1fUu//vvP65evcrRo0c5evQos2fPpkiRImzduhUXFxdNgn3//n0ge/8+bPMLZ8+ezc2bNylRooRmTcnUJE9ShRCOpdPpqFSsAJWKFaBPwzLODifHSXIoHE6v1zM0uA1Dg6WsthDCuT7//HOHncvenLWsyOhSFun5/vvviYyM5LXXXsPPz48rV65w8eJFGjduTGJiIqVLl6Z///507dqVmjVr0qpVK37//XdGjBjBrFmzGD16NPPnzwdg5cqV/PfffzRt2pRWrVqluFZ4eDh9+/ZFURR69epFhw4dgAc9l23bttXsP2nSJFq3bs2UKVPw9fVVew2nTp2aoURt27ZtQFJCbts/O2tW2qtae/v2bY4dO8adO3cICQnh2rVrXL58mUuXLnH9+nWAFNU/AwIC+OGHH0hISKB58+Yp1jiEzBWkseeff/5Rh+O+9957mqQ4+bxHIYTICZIcCiGEELnIEUtZxMbG8sknnwAwbNgwADZv3gwk9SJt3ryZsLAw7t69q66TZ5uz98EHH7Bs2TJ+++03tXdq0aJFNGzYkP79+3Py5EnNnEVFUejbty+XLl2iRIkSzJo1C4CzZ8+ybt06QFv1E6BVq1a0bNmSjRs3MmLECCBp6Y327dtn6PnZCtc899xzapujlyUJDQ1Nd8jmpEmTCA4OpkqVKpQqVQqDwaD2ntoSZJvsDCu12bBhA7169SI6OppmzZqpxWn27dvHoUOH2LNnD6CdfymEEI4kS1kIIYQQucgRS1nMnj2bkJAQXnjhBWrWrAnAkiVLAGjTpg1Lly4FoGfPnupyCbb5aL6+vqxevZpz586pS2DUq1ePUaNGERoaqiZzNuPHj1eTwK+//lpdm2/UqFFYrVbatGlD1apVU8T4xhtvqL8bjUYmTpyYoftz6NAh9u3bByTNz3QEe3NJq1evTqlSpTAajdSvX5/Ro0ezcuVKzp07R6lSpQAYMWIErVq1oly5chiNRtatW8eJEyfw8vKiWzftfPqHk8Pbt2+nGo9tsfuwsDCOHTsGJC1nsXLlSu7fv0+5cuX44Ycf1N7SO3fuMGzYMFasWAGk7NEUQghHkZ5DIYQQ+Zoj10K0nSu7FU3TcvbsWSZPngzAhAkTgKQ5ert27aJIkSJUqlSJNWvWUK5cOZo0acKNGzcA7bw729IbFouFPn36MH78eD744ANWrlzJ9evX1fmKs2bNYurUqQB069ZNHT66ZMkSNmzYgE6n44MPPkgR45o1axg4cKC6bTabqV+/Pu+99x6DBg1Ks3roO++8A0CdOnU0lTqT/3/K6JIWERER6vO0Z82aNVSoUCHFQvP2/k1ERUUxatQoAAYPHoyvr7aEvS05tPXUjh07llu3bjFhwgRNMrdy5Ur69esHJCWQNWvWpH379vTs2ZN58+YxdOhQ/Pz8KFy4sHpMixYt6NixI1WrVqVRo0bp9ngKIURWSXIohBAiX0tefdRsNme5eAk8SEJs5yxVqpS6FEJUVBQAL7zwAi4u9hdeBtRkzlbsJDY2lnXr1lGnTh0SExPp0aMHMTExNG7cWB12+d577wEPKoWaTCb69u2rGYp59uxZLBaLZg7b2rVrWb58Obt27eLKlSv89ddfVKxYUT2uVatWfPLJJ5jNZnU46a5duxg0aBAAffv21SzncPLkScaNG8f69esB6N69OxMnTmTo0KFs376dUaNG8dFHH9G1a1deffVV6tevr7nfn332mTo8dvjw4Zr7YrVa1d8zsuRIcqlVmE0eu71rJU8qR4wYwZUrV/D19VUT2ORiY5MWzL548SIxMTHs3r2bCxcu0KNHDzU5nDNnDiNGjEBRFAYPHkyZMmVYu3Ytq1evZvXq1RiNRmrUqEGNGjUoU6YMQUFBFCxYEF9fX8aOHYvBYMBgMHD8+HFMJhMmk4mEhASio6OJiIggIiKCF1980SEFmIQQ+ZQi8oxr164pgAIo165dc3Y4QohMOnv2rHLq1Cnl7Nmzzg5FJPPTTz+pr60xMTHZOtehQ4cUQHF1dVUURVEKFCig+Pn5KUWLFlWKFy+uBAYGZvinePHiSuHChRVPT09l3759iqIoSmJiorJs2TKlSJEiytatWxVFUZQlS5YogGI0GpVLly4pBw4cUGrWrKm+T0RFe8SZ+gAAGEJJREFURanPr0iRIkrlypWVypUrK+XKlVN0Op0CKJ988kmqz+nYsWPKqlWr1O3du3crr7zyilKqVCnl3r17itVqVbZt26a0adNGPV+ZMmWU9evXq8dYrVZl4cKFSmBgoBoLoHh6eiqjR49W99u2bZvSu3dv5amnnlLMZrMmjpCQEPW4jFq4cKECKA0aNMjwMYqiKAEBAQqgXL9+XVEURRk/frx67S+//NLuMR9++KHmuQFKoUKF1H9Tv//+u9r+xhtvaI49fPiwMmjQIKVw4cIpzpGZH39/fyU6OjpTz1Vel4TIu3IiN5DkMA+R5FCIvE0+hD2a5s+fr7623r9/P1vn2r17t3ouq9XqmADtCA0NVX//+++/lUqVKimDBw9W2x6+9ujRoxV3d/cUyYSbm5vSrVs3JT4+PtMx3L9/X1m/fr1SqlQp9XwlS5ZU5syZoyQkJNg9Jj4+Xpk7d65SsWJFBVBKly5t956bTKYUbVevXs1yclixYsUMH6MoilKoUCEFUC5duqQkJCQo77zzjuLp6ak0aNBAsVgsdo+5e/eu8sILLyguLi4KoPj5+SkLFizQ7PPNN98o1apVU2JjY+2ew2KxKEePHlUWLlyovPXWW8qrr76qNG7cWKlSpYpSrFgxxcPDI83ksEePHpl6nooir0tC5GU5kRvoFMWBky1Ejrp+/TpBQUEAXLt2ze4ix0KIR9e5c+fUYYvJ51IJ55o2bZo6TPDWrVsEBAQ4OaLMu3fvHmazmaJFi+b6tX/66ScWLlzIwIEDadu2bYaH5e7cuRODwaCuH5mes2fPqsMlc/qji4eHB/Hx8Zw8eZInnngCgAsXLqDT6ShXrlyaxyr/X1gotaU3Hh7am1lWq5XY2Fji4uKIi4sjISFB/QkICCAwMDBT55PXJSHyrpzIDSQ5zEMkORQib5MPYUKIR428LgmRd+VEbiBLWQghhBBCCCGEkORQCCGEEEIIIYQkh0IIIYQQQgghkORQCCGEEEIIIQSSHAohhBBCCCGEQJJDIYTINbby9RaLJcdL8QshRHoURcFisQCkuvSGECJ/kVcCIYTIJa6urkDSB7LY2FgnRyOEyO8SEhLUL6psr09CiPxNkkMhhMglPj4+6u/37t2T3kMhhFNFRkaqv3t5eTkxEiHEo0KSQyGEyCXe3t7odDoAoqOjuX79OjExMZIkCiFylcVi4e7du9y9e1dt8/b2dmJEQohHhdHZAQghRH6h1+sJDAzkxo0bKIpCdHQ00dHR6HQ6dT6iEELkpOTzDG2KFCkiw0qFEIAkh0IIkasKFCigSRAh6cOa2Wx2cmRCiPzI19cXf39/Z4chhHhESHIohBC5rECBAlSqVIno6GgiIyMxmUwpvskXQoicYjAY8PT0xM/PD3d3d2eHI4R4hEhyKIQQTqDX6/Hx8dEUqRFCCCGEcCYpSCOEEEIIIYQQQpJDIYQQQgghhBCSHAohhBBCCCGEQJJDIYQQQgghhBBIciiEEEIIIYQQAkkOhRBCCCGEEEIgyaEQQgghhBBCCCQ5FEIIIYQQQgiBJIdCCCGEEEIIIQCjswMQGWc2m9Xfb9265cRIhBBCCCGEEM6UPB9InidkhySHeUhYWJj6e7169ZwYiRBCCCGEEOJRERYWRpkyZbJ9HhlWKoQQQgghhBACnaIoirODEBkTHx/P8ePHAShSpAhGY97s+L1165ba83ngwAGKFy/u5IjyD7n3ziP33nnk3juH3HfnkXvvPHLvnSc/3nuz2ayOLKxevTru7u7ZPmfezC7yKXd3d+rWrevsMByqePHilCxZ0tlh5Ety751H7r3zyL13DrnvziP33nnk3jtPfrr3jhhKmpwMKxVCCCGEEEIIIcmhEEIIIYQQQghJDoUQQgghhBBCIMmhEEIIIYQQQggkORRCCCGEEEIIgSSHQgghhBBCCCGQ5FAIIYQQQgghBKBTFEVxdhBCCCGEEEIIIZxLeg6FEEIIIYQQQkhyKIQQQgghhBBCkkMhhBBCCCGEEEhyKIQQQgghhBACSQ6FEEIIIYQQQiDJoRBCCCGEEEIIJDkUQgghhBBCCIEkhyIHWa1WZ4eQb8m9dx65984h99155N47j9x755F77xxy33OeJIfCoc6fP0/37t0pVKgQBoMBg8HAM888w5IlS5wd2mMvL957k8nk7BAcIi/e+8eB3HfnkXvvPHLvnScv3vvH4X02L973PE0R+crgwYMVQLl06VKG9jebzUrr1q2VjPxTOXz4sOLr66sAitFoVCpWrKh4eXkpgAIo//vf/7IZvVZUVJQybtw4pWbNmoqfn59SoEABpUqVKsqgQYOU8+fPp3ns1q1blebNmysFChRQvLy8lGbNmikHDhxwaHwPk3uvKPfv31feeustpVy5corRaFQAxcfHR+nevbty9uxZh8aYnNx7+xITE5UGDRoogLJt2zaHxqkoct/tWbJkiQIobm5uyvHjxx0aY3Jy7xUlNDRUGTZsmPLkk08qBQoUUPz8/JTq1asrb7zxhnLr1i2Hxpjc43bvP//8c6Vx48aKr6+v4uHhodSsWVP55JNPlMjIyDSPlffZ7MnqvXfG+6zcd/ty+j02p0hymI9MnTpV/WPK6B/wkCFD1GPSEhcXpzzxxBMKoHTv3l25e/eu2j5lyhT1HFu2bMnu01AURVHu3r2rVKtWTT2vXq9XfwcUd3d3Zf369XaPXbBggaLT6dR9bb+7ubkpmzZtckh8D5N7ryhXrlxRypYtq97zJ554Qqlbt67mzWvfvn0OiTE5ufepe+edd9RjHf3GJfc9pWvXril+fn4KoEydOtUhsdkj915RLl68qJQoUSLV4woVKqTs37/fITEm9zjd+5MnT6qv2bZ7mPy9s3z58srp06ftHivvs9mT1XvvjPdZue+py8n32JwkyWE+MWPGDM0/8PT+gM1mszJ69GjNm2lavvzySwVQ6tevr1gslhSPv/TSSwqg9O3bNztPQ9W7d28FUJo2baocP35cSUxMVKKjo5Vt27Yp9evXV9/8w8PDNcft3r1bMRgMCqC0aNFCuXDhgpKYmKgsX75ccXNzU0qUKJHimOySe68oVqtVfaxOnTrKsWPH1MfOnDmjlC5dWgGUSpUqKVar1SFxKorc+7Rs3rxZ84HZkW9cct9TslqtSrNmzRRAadCggWI2mx0S28Pk3idp0qSJAiidO3dWLly4oFgsFiUiIkL57bfflMqVKyuAUrlyZYf+f3ic7n1kZKRSvnx5BVBKliyprFu3TklMTFSioqKUhQsXql9ylCpVKkVvirzPZk9W770z3mflvqcuJ99jc5okh4+5mJgYpWvXrgqgBAUFZegPOCwsTHn++edTHJOW5s2bKzqdLtVvb8aNG6cAyosvvpidp6MoiqIkJCQobm5uil6vV27fvp3i8Xv37qlvTH/88YfmseDgYAVQnnrqKSU+Pl7z2OTJkxVAefvtt7Mdo6LIvU9+71esWKG+Kdn7ULB+/Xr1uTri23y593/YOcMDISEhSrFixRR48I2+I9645L6nft9nzZqlAIqHh4dy5syZbMf1MLn3D+795cuXFUApUqRIitd5RVGUo0ePqs/11KlT2Y7zcbz3n332mQIoLi4udoc/b9q0SY15xowZmsfkfTZ7snrvc/N9Vu77DDtneCCn3mNziySHj7m33npLAZTg4GDl1q1bGfoD7ty5swIobdq0UY4dO5ahP2BFSRqnnZpevXopgNK7d++sPA2NGzduKJD0bXFqbN/wrF69Wm07ePCg+lzWrVuX4pg7d+4oHh4eSvny5bMdo6LIvU9+7z/99FOlbt26mrbkQkJC1Of6yy+/ZDtOuff277OiJH27/OKLLyqQ1Kti+zbZEW9cct/t3/fTp08rHh4eCqB88cUX2Y7JHrn3D+79nj17FECpVauW3WPu37+vPteDBw9mO87H8d7bErwuXbqkuo9tuG+nTp3UNnmfdd69z833WbnvnVLdJyffY3OLVCt9zOn1eqZMmcLu3bsJCAjI0DEeHh58/fXX/PrrrxQoUCDD1/L29rbbfu3aNdasWQNAu3btMny+1BQuXBg3Nzfu3bvH/v37Uzy+a9cuwsPDAahVq5bavnPnTjXOVq1apTjO39+fevXqceHCBc6dO5ftOOXeP7j3Y8aM4cCBA3To0MHueW/evKn+npnnnRq597VSPG4zbdo0Nm3aRPny5VmwYEG240pO7nvK+242m+nduzdxcXE8++yzjBw5Mtsx2SP3/sG9DwwMBODkyZNcvXo1xXHr168HwN3dnSeeeCLbcT6O9/7MmTMANG3aNNV9goKCAIiIiFDb5H22XYbPl5qs3vvcfJ+V+x6R6j45+R6ba5ydnYqcZTKZNNtk4Nud5MdcunQpw9/uPMxisSgbNmxQx3C//PLLmT5HagYNGqRA0tjvX3/9Vbl//74SFham/PTTT0pgYKACKD179tQcM3ToUAVQnnvuuVTPO2LECAVQfv3112zHKPc+4yZMmKAAipeXlxIdHZ3tGOXe27d3717FaDQqbm5uyqFDhxRFURz6rabc95RsRRN0Op3y3nvvKW+//bYycuRIZdasWcqNGzccFqPcey3bN/fVq1dXtm/frkRGRio3b95U5s+fr1Y+nDhxokNifBzvfeHChRVAWbRoUar71KpVSwGUPn36qG3yPpt9Wb336XHk+6zc9z52H8/p99jcIslhPpORP+DksvoHPGzYMKVQoULqsX379rU79yOr4uPjlaFDh2omQtt+XFxclFGjRikJCQmaY7p06aJA2mWPbR/kZs2a5bBYbfLzvU9LWFiYOjRs1KhRDoszObn3SUPpbG9Sc+fOVdtz8o0rv9/3c+fOKW5ubuq+1atXV9q2bavUrl1b0el0iru7uzJ9+nSHxZlcfr/39+7dU4exPfzj5eWlfPzxxw4tfpXc43Dv6/xfe3cX0tQfxgH8u+narPlPezESqhUYDAmCCYmEJOZdGRWI3VRIXaSJ9CZFBUWYRZQ3iSFF0AtdFBRRJoOoXfWCZFgYKRaLJEkLtZYTZ8//QnbSvaUd53Z2vh8YxM6Lj9+az/nZOb9fTo4AkMrKypDbe3t7lX/bt27dUt5nn1XvX7OPJNp9lrnHpsdGC28rpajx+XzKn9vb26flFhI/r9eLwcFBiEjQNovFAq/Xi+/fv094f3h4GACQlpYW9rzp6ekAgC9fvkxbrbEQb9lHUllZif7+fmRkZOD48ePTVmesxGv2u3btgtvtRklJCfbs2TNtNcWLeMz94MGDGB4ehtVqhdPpRFtbG+7fv4+Wlha8efMGNpsN1dXVOHHixLTVGgvxmP3Q0BB+/PgR8pwpKSkYGBgIu11LopV9SUkJAODatWvo6uoK2r5v3z4MDw9j0aJF2Lx5s/I++6x6/5p9JInUZ+M194TqsTEdmtKMwwz9dkdkbPHPpqYmWbVqlQBjM8d1dXVN+TyBRkdHpaioSKnLarVKfn6+rFmzZsJv6TMzM+Xz58/KcVu3bhUAcuLEibDnbmxsFABSVVWlus5Aes4+nKtXryrH3Lt3T3V94eg9+/r6egEgWVlZQVNwJ8L/HIrEX+6dnZ3KtrNnz4Y8t39SBpPJJH19faprHU/P2Xs8HqUWAJKeni4FBQXicDiU2U0BSHZ2dsTJLv5VImTv9XqV5UBWrFghDx8+lL6+Pnnx4oUySyUQPGsj+2zssg9nJvqs3nOPVY+NFg4OdWYmP8B+3759UxYVjTTD02TdvXtXqam8vFw8Ho+yrbu7e8KHuLy8XNnmn9WqpqYm7LkvX74sAGT37t2q6wyk5+xDef36tTKD44EDB1TXFomes29raxOLxSIWi0VaW1uDzpsog0O/eMndfwGcnJwcchkGP/9zMzdv3lRd63h6zr6urk55/9SpUzIyMqJs6+zslMLCQmV7NG7rTYTsRcYulo8eParcjhj4Wr58edAtfeyzscs+lJnqs3rOPZY9Nlo4ONSZWHyARURqamoEgKSlpak6j4jIzp07BRhbFDnUMyOjo6Oydu1aASA2m015v6qqSgBIRUVF2HOfO3dOAMiuXbtU1xlIz9kH+vr1q/IDc/369VFbENxPr9l7PB6x2+0CQBoaGkKeN9EGhyKxz318DVlZWRHPn5eXJwCktrZWda3j6Tn7devWCRB+0oihoSFlUL5u3TrVdQZKhOzH8/l80t7eLs+fP5fa2lql1lDLIbDPxi77QDPZZ/Wae6x7bLTwmUOaFh6PR5kGOJSsrCwAmJZnPLq7uwEAGzduhMFgCNpuNBqV+8HHP9OwdOlSAMDHjx/DnrunpwdA+KmT45EWsh9vaGgImzZtgtvtht1ux+3bt5GUlKS6tliI9+xfvnyJd+/eAQD27NkDg8EQ9HK73QCAgoICGAyGiNN4x4t4zx3488xVpGevAGBwcBAAMGfOHNW1zgQtZO8/rri4OOR5LRaLssyClp57m8nsx0tKSoLdbofD4cDVq1cBAEVFRSGfu2KfjV324yVKn4333BO1xybHugDSvt7eXixatAjJyclwu91YvHhx0D7+taYyMzNVfz3/RZTJZPrrvv4H3wEgJycHAPDq1SuISMiLjefPnwP4s5ZNvNNK9n4+nw+lpaV49uwZMjIy0NTU9NeL53ilheyNRiPMZnPEff0TSJhMJhiNRsyaNUtlpdGlhdwBIDs7GwDQ0dGB379/w2gM/l3swMCAcuFjt9tV1xptWsle7c+peDTT2Ydy5coVdHR0YNasWbh48WLIfdhnY5e9X6L0WS3knog9FgD4P4ek2sKFC5GTk4ORkZGQCzz7fD5cunQJAFBYWKj66/kvotra2sLu8+TJEwDA2rVrlfdyc3NhtVrR09MDl8sVdEx/fz9aWloARF5EPJ5oJXsAGBkZQUlJCe7fvw+r1YqmpibYbDbVNcWKFrLPz8+H1+uN+Fq2bBkAwOl0wuv1wul0qq41mrSQOwDk5eUhLS0NAwMDaGhoCHnc6dOnMTIygvT09KDPSzzSSvaTOe7p06dBx8Wzmc4+0M+fP3Hy5EkAQHV1NVauXBlyP/bZ2GUPJFaf1ULuidhjAai82Zc0B1G6L/zBgwfKflu2bJE3b96Iz+cTt9stmzdvFgCSkpIi79+/V/09vH37VpKSkmTu3LnS2dkZtP3OnTtiMBjEYDDIs2fPJmzbvXu3AJDc3NwJkxSIiBw/flwAyLx586Z1nS4/vWe/Y8cOAcZmZmxublZdy1ToPftItPjMoVZyv3DhggAQs9ksFy5cUGYk/fDhg1RUVCjfw3Q/byii7+ybm5sFgCxdujTkLLDnz59X6pxsPlORCNkHOnjwoABjszj++vUr4r7ss9NrKtnHqs/qPfdItPjMIQeHOhOtD7DI2MK24xcqHv/n//77Tx49ehR0jMvlErPZLGazWVwu16S/j/r6eklKSpLU1FQpKyuTmpoaOXLkiDIRAQA5ffp00HHt7e2SnJwsAGTDhg3S1dUl/f39cubMGTEajQJAjh07Nuk6pkLP2b99+3ZCbf6vG+qltUkK4j37v9Hi4FBEG7n7fD6prKxU9gEwYSkF/wXP6OjopGuZLL1nf/jwYQEgCxYskPLycqmtrZVDhw6Jw+FQ/h6uX78+6TqmIlGy93v37p2YTCYBEPL8gdhn/5jJ7GPZZ/Wc+99wcEhxL5ofYJGxD2RxcbHMnz9fTCaT2Gw22bt3r3z69Cnk/k+ePFHOP9UPTmtrq5SVlUlWVpbMnj1bTCaTLF68WDZt2iROpzPscY2NjUqDCnw5HI4J06VPJz1nf+PGjZB5h3qFm2FQDT1n/zdaHRyKaCd3l8sl27dvl5UrV8qcOXMkJSVFVq9eLXV1dVGbQZDZizx9+lRKS0vFZrOJxWIRs9ksS5YskW3btsmLFy+mVMNUJFL2IiLr168XAFJaWjrpY9hnx8xk9rHss3rO/W+0ODg0iIiASGceP36M/fv3K8+kmEwmbN++HXV1dUhNTY1xdURERNrGPkukTRwckq51dnbi27dvsNvtmDt3bqzLISIiSijss0TawsEhERERERERcSkLIiIiIiIi4uCQiIiIiIiIwMEhERERERERgYNDIiIiIiIiAgeHREREREREBA4OiYiIiIiICBwcEhERERERETg4JCIiIiIiInBwSERERERERODgkIiIiIiIiMDBIREREREREYGDQyIiIiIiIgIHh0RERERERAQODomIiIiIiAgcHBIRERERERE4OCQiIiIiIiJwcEhERERERETg4JCIiIiIiIjAwSERERERERGBg0MiIiIiIiIC8D+1YTH3crNrvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= data_features_1[['经度']]\n",
    "y =data_features_1[['纬度']]\n",
    "j= data_features[['经度']]\n",
    "k =data_features[['纬度']]\n",
    "plt.figure(figsize=(5, 5), dpi=200)\n",
    "plt.plot(data_features_2['经度'], data_features_2['纬度'],label = '原始轨迹')\n",
    "plt.plot(x, y,label='卡尔滤波后轨迹')\n",
    "plt.plot(j, k,label='中值滤波后轨迹')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# #两算法结合的结果\n",
    "# # x= data_features_1[['经度']]\n",
    "# # y =data_features_1[['纬度']]\n",
    "# j= data_features[['经度']]\n",
    "# k =data_features[['纬度']]\n",
    "# plt.figure(figsize=(5, 5), dpi=200)\n",
    "# plt.plot(data_features_2['经度'], data_features_2['纬度'],label = '原始轨迹')\n",
    "# # plt.plot(x, y,label='卡尔滤波后轨迹')\n",
    "# plt.plot(j, k,label='中值滤波后轨迹')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6ec32421",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.399264Z",
     "start_time": "2024-08-07T01:17:20.396639Z"
    }
   },
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "# data_features_1.to_excel(\"D:/data/0280kaer.xlsx\",index=True,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ac5c8c38",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.404022Z",
     "start_time": "2024-08-07T01:17:20.400284Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 初始化KalmanFilter \n",
    "# kf = KalmanFilter(process_variance=0.01, measurement_variance=0.1)  \n",
    "\n",
    "# measurements = []  \n",
    "# for index, row in df.iterrows():  \n",
    "#     # 提取经度和纬度，并创建NumPy数组  \n",
    "#     measurement = np.array([[row['x']], [row['y']]])  \n",
    "#     # 将NumPy数组添加到measurements列表中  \n",
    "#     measurements.append(measurement)  \n",
    "    \n",
    "# estimated_points = []\n",
    "  \n",
    "# # 对每个观测值进行更新  \n",
    "# for z in measurements:  \n",
    "#     kf.update(z)\n",
    "#     x = kf.get_state()[0, 0]  # 注意这里使用了numpy的索引方式，假设get_state()返回的是numpy数组  \n",
    "#     y = kf.get_state()[1, 0]  \n",
    "#     estimated_points.append((x, y))  # 将估计的(x, y)坐标添加到列表中\n",
    "    \n",
    "    \n",
    "# plt.figure(figsize=(10, 5))  # 可选：设置图形的大小  \n",
    "# x_coords, y_coords = zip(*estimated_points)  # 解包列表以获取x和y坐标的元组  \n",
    "# plt.plot(x_coords, y_coords, 'ro-')  # 使用红色圆圈和线连接点  \n",
    "# plt.xlabel('X')  # 设置x轴标签  \n",
    "# plt.ylabel('Y')  # 设置y轴标签  \n",
    "# plt.title('Estimated Points Over Time')  # 设置图形标题  \n",
    "# plt.grid(True)  # 显示网格  \n",
    "# plt.show()  # 显示图形\n",
    "# #     print(kf.get_state())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "92cf8c25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.407708Z",
     "start_time": "2024-08-07T01:17:20.405031Z"
    }
   },
   "outputs": [],
   "source": [
    "# data_features['定位时间']=pd.to_datetime(data_features['定位时间'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "64b4bf40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.410945Z",
     "start_time": "2024-08-07T01:17:20.408713Z"
    }
   },
   "outputs": [],
   "source": [
    "# data_features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1edde8fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.415514Z",
     "start_time": "2024-08-07T01:17:20.411954Z"
    }
   },
   "outputs": [],
   "source": [
    "# data_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "88f9cbab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.418432Z",
     "start_time": "2024-08-07T01:17:20.416518Z"
    }
   },
   "outputs": [],
   "source": [
    "# x= data_features[['定位时间']]\n",
    "# y =data_features[['经度']]\n",
    "# plt.figure(figsize=(5, 5), dpi=200)\n",
    "# # plt.plot(data_features_2['经度'], data_features_2['纬度'],label = '原始轨迹')\n",
    "# plt.plot(x, y,'ro',label='卡尔滤波后轨迹')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1a57f7bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.422519Z",
     "start_time": "2024-08-07T01:17:20.419437Z"
    }
   },
   "outputs": [],
   "source": [
    "# x= data_features[['定位时间']]\n",
    "# y =data_features[['纬度']]\n",
    "# plt.figure(figsize=(5, 5), dpi=200)\n",
    "# # plt.plot(data_features_2['经度'], data_features_2['纬度'],label = '原始轨迹')\n",
    "# plt.plot(x, y,'ro',label='卡尔滤波后轨迹')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "701b2216",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.425117Z",
     "start_time": "2024-08-07T01:17:20.423522Z"
    }
   },
   "outputs": [],
   "source": [
    "# data_features_2['经度']=Denoising_wavelet(data_features_2['经度'])\n",
    "# data_features_2['纬度']=Denoising_wavelet(data_features_2['纬度'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f4d09dcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.489787Z",
     "start_time": "2024-08-07T01:17:20.487135Z"
    }
   },
   "outputs": [],
   "source": [
    "# x= data_features_2[['经度']]\n",
    "# y =data_features_2[['纬度']]\n",
    "# plt.figure(figsize=(5, 5), dpi=200)\n",
    "# plt.plot(data_features_3['经度'], data_features_3['纬度'],label = '原始轨迹')\n",
    "# plt.plot(x, y,label='小波去噪后轨迹')\n",
    "# # plt.plot(j, k,label='中值滤波后轨迹')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a08861e",
   "metadata": {},
   "source": [
    "# 采用DBSCAN对时间和距离聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "58b2109a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:20.924096Z",
     "start_time": "2024-08-07T01:17:20.919401Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11125771817952\n"
     ]
    }
   ],
   "source": [
    "#两坐标点的距离\n",
    "import math\n",
    "\n",
    "R = 6371.393\n",
    "Pi = math.pi\n",
    "\n",
    "# A地\n",
    "jingduA, weiduA= 110.78927, 32.68965\n",
    "xA = math.cos(math.radians(weiduA))*math.cos(math.radians(jingduA))\n",
    "yA = math.cos(math.radians(weiduA))*math.sin(math.radians(jingduA))\n",
    "zA = math.sin(math.radians(weiduA))\n",
    "\n",
    "# B地\n",
    "jingduB, weiduB = 110.78927, 32.68966\n",
    "xB = math.cos(weiduB*Pi/180) * math.cos(jingduB*Pi/180)\n",
    "yB = math.cos(weiduB*Pi/180) * math.sin(jingduB*Pi/180)\n",
    "zB = math.sin(weiduB*Pi/180)\n",
    "\n",
    "\n",
    "# 开始计算\n",
    "cosalpha = (xA*xB+yA*yB+zA*zB)/((xA*xA+yA*yA+zA*zA)*(xB*xB+yB*yB+zB*zB))**0.5\n",
    "\n",
    "alpha = math.acos(cosalpha)\n",
    "L = alpha * R\n",
    "\n",
    "# 单位是米\n",
    "print(L*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "adb2b14a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-21T02:27:00.778868Z",
     "start_time": "2024-08-21T02:27:00.775039Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8896142862888569\n"
     ]
    }
   ],
   "source": [
    "import math  \n",
    "  \n",
    "R = 6371.393  # 地球半径，单位为千米  \n",
    "  \n",
    "# A地  \n",
    "jingduA, weiduA = 75.3884, 40.507145\n",
    "# B地  \n",
    "jingduB, weiduB = 75.388400, 40.507153\n",
    "  \n",
    "# 将经纬度转换为弧度  \n",
    "latA, lonA = math.radians(weiduA), math.radians(jingduA)  \n",
    "latB, lonB = math.radians(weiduB), math.radians(jingduB)  \n",
    "  \n",
    "# 哈弗辛公式  \n",
    "dlat = latB - latA  \n",
    "dlon = lonB - lonA  \n",
    "a = math.sin(dlat/2)**2 + math.cos(latA) * math.cos(latB) * math.sin(dlon/2)**2  \n",
    "c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))  \n",
    "  \n",
    "distance = R * c  # 距离，单位为千米  \n",
    "  \n",
    "# 转换为米  \n",
    "print(distance * 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a22c9cc9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:21.169860Z",
     "start_time": "2024-08-07T01:17:21.167197Z"
    }
   },
   "outputs": [],
   "source": [
    "# data_features['定位时间']=pd.to_datetime(data_features['定位时间'])\n",
    "# data_features['定位时间']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "282d2203",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:17:21.394089Z",
     "start_time": "2024-08-07T01:17:21.391423Z"
    }
   },
   "outputs": [],
   "source": [
    "# from datetime import datetime, timezone\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "  \n",
    "# # 假设的过去时间点（UTC时间）  \n",
    "# for i in range(0,len(data_features)):\n",
    "#     past_time_str = data_features['定位时间'][i]\n",
    "#     past_time_format = \"%Y-%m-%d %H:%M:%S\"  \n",
    "#     past_time_utc = datetime.strptime(past_time_str, past_time_format).replace()\n",
    "    \n",
    "#     # 获取当前时间（UTC时间）  \n",
    "#     current_time_utc = datetime.now()\n",
    "\n",
    "#     # 计算时间差（秒）  \n",
    "#     delta = current_time_utc - past_time_utc\n",
    "#     seconds_since_past = delta.total_seconds()\n",
    "\n",
    "#     data_features['定位时间'][i]=seconds_since_past"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "3cf99f19",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:07.270756Z",
     "start_time": "2024-08-07T01:58:07.263802Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276\n",
       "...                   ...         ...        ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810\n",
       "\n",
       "[8559 rows x 3 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data_features[['定位时间','经度','纬度']]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "6274134f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:07.450198Z",
     "start_time": "2024-08-07T01:58:07.448231Z"
    }
   },
   "outputs": [],
   "source": [
    "# data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "31ec16a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:07.652142Z",
     "start_time": "2024-08-07T01:58:07.647950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[113.797464,  22.688276],\n",
       "       [113.797464,  22.688276],\n",
       "       [113.797464,  22.688276],\n",
       "       ...,\n",
       "       [113.943112,  22.68881 ],\n",
       "       [113.943112,  22.68881 ],\n",
       "       [113.943112,  22.68881 ]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point=np.array(data[['经度','纬度']])\n",
    "point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "537543e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:08.519683Z",
     "start_time": "2024-08-07T01:58:07.855734Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN  \n",
    "from sklearn.datasets import make_blobs  \n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.cluster as skc\n",
    "  \n",
    "# 生成一个随机数据集  \n",
    "# X, y = make_blobs(n_samples=1000, centers=8, random_state=42)\n",
    "  \n",
    "# 创建DBSCAN聚类器  \n",
    "dbscan = DBSCAN(eps=0.0001, min_samples=10).fit(point) #20*15/60=5分钟\n",
    "\n",
    "# 进行聚类\n",
    "labels = dbscan.labels_ #labels为每个数据的簇标签\n",
    "\n",
    "# 可视化聚类结果\n",
    "plt.scatter(point[:, 0], point[:, 1], c=labels, cmap='rainbow')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "2d6e391b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:08.527483Z",
     "start_time": "2024-08-07T01:58:08.520687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度  labels\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276       0\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276       0\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276       0\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276       0\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276       0\n",
       "...                   ...         ...        ...     ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810       4\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810       4\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810       4\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810       4\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810       4\n",
       "\n",
       "[8559 rows x 4 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['labels']=labels\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b7ff687c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:08.532124Z",
     "start_time": "2024-08-07T01:58:08.528489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_clusters_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "393661a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:08.539263Z",
     "start_time": "2024-08-07T01:58:08.533128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7949</td>\n",
       "      <td>7949</td>\n",
       "      <td>7949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        定位时间    经度    纬度\n",
       "labels                  \n",
       "-1       177   177   177\n",
       " 0        28    28    28\n",
       " 1        91    91    91\n",
       " 2       299   299   299\n",
       " 3        15    15    15\n",
       " 4      7949  7949  7949"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data['labels']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "8ee6c799",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:08.867128Z",
     "start_time": "2024-08-07T01:58:08.863888Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# lei=5\n",
    "# count=0 # 一个类的数量计数\n",
    "# num=0\n",
    "# for i in range(0,len(data)):\n",
    "#     if(data['labels'][i]==0):\n",
    "#         count=count+1\n",
    "#         #仅针对最后一类数据做二级分类处理\n",
    "#         if(i==len(data)-1):\n",
    "#             num=num+1\n",
    "#             if(num>1):\n",
    "#                 for k in range(i-count,i):\n",
    "#                     data['labels'][k]=lei\n",
    "#                     lei=lei+1\n",
    "#     else:\n",
    "#         if(count!=0 and count<=11):#筛选出聚类结果当中时间小于5分钟的经纬度归为离散数据-1\n",
    "#             for j in range(i-count,i):\n",
    "#                 data['labels'][j]=-1\n",
    "#         elif(count!=0 and count>11):#筛选出聚类结果当中时间超过5分钟的经纬度\n",
    "#             num=num+1\n",
    "#             if(num>1):#如果在聚类当中的时间段分离（即类中分段）则按时间规则将聚类结果进行二级分类\n",
    "#                 for k in range(i-count,i):\n",
    "#                     data['labels'][k]=lei\n",
    "#                 lei=lei+1\n",
    "#         count=0\n",
    "# print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "172c3824",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:09.420219Z",
     "start_time": "2024-08-07T01:58:09.415635Z"
    }
   },
   "outputs": [],
   "source": [
    "#将不在时间范围内的数据标签改为默认值-1\n",
    "def function_1(x,lei):\n",
    "    count=0\n",
    "    num=0\n",
    "    for i in range(0,len(data)):\n",
    "        if(data['labels'][i]==x):\n",
    "            count=count+1\n",
    "            #仅针对最后一类数据做二级分类处理\n",
    "            if(i==len(data)-1):\n",
    "                num=num+1\n",
    "                if(num>1):\n",
    "                    for k in range(i-count,i):\n",
    "                        data['labels'][k]=lei\n",
    "                        lei=lei+1\n",
    "        else:\n",
    "            if(count!=0 and count<=11):#筛选出聚类结果当中时间小于5分钟的经纬度归为离散数据-1\n",
    "                for j in range(i-count,i):\n",
    "                    data['labels'][j]=-1\n",
    "            elif(count!=0 and count>11):#筛选出聚类结果当中时间超过5分钟的经纬度\n",
    "                num=num+1\n",
    "                if(num>1):#如果在聚类当中的时间段分离（即类中分段）则按时间规则将聚类结果进行二级分类\n",
    "                    for k in range(i-count,i):\n",
    "                        data['labels'][k]=lei\n",
    "                    lei=lei+1\n",
    "            count=0\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "3a5030e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:11.273565Z",
     "start_time": "2024-08-07T01:58:11.135786Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "5\n",
      "0\n",
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_13424\\3859870029.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['labels'][j]=-1\n",
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_13424\\3859870029.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['labels'][k]=lei\n"
     ]
    }
   ],
   "source": [
    "function_1(0,5)\n",
    "function_1(1,5)\n",
    "function_1(2,5)\n",
    "function_1(3,5)\n",
    "function_1(4,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "43313a10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:28.235300Z",
     "start_time": "2024-08-07T01:58:28.231601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_clusters_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5ca15a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num=0\n",
    "# count=0\n",
    "# for i in range(0,len(data)):\n",
    "#     if(data['labels'][i]== 3):\n",
    "#         count=count+1\n",
    "# #         print(i)\n",
    "#         if(i==len(data)-1):\n",
    "#             print(count)\n",
    "#             num=num+1\n",
    "#     else:\n",
    "#         if(count!=0):\n",
    "#             print(count)\n",
    "#             num=num+1\n",
    "#         count=0\n",
    "# #     num=num+1\n",
    "# print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "05edf246",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-07T01:58:32.075253Z",
     "start_time": "2024-08-07T01:58:32.068097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7947</td>\n",
       "      <td>7947</td>\n",
       "      <td>7947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        定位时间    经度    纬度\n",
       "labels                  \n",
       "-1       214   214   214\n",
       " 0        16    16    16\n",
       " 1        21    21    21\n",
       " 2        44    44    44\n",
       " 4      7947  7947  7947\n",
       " 5       212   212   212\n",
       " 6        24    24    24\n",
       " 7        55    55    55\n",
       " 8        26    26    26"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(data['labels']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2eab278f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>定位时间</th>\n",
       "      <th>经度</th>\n",
       "      <th>纬度</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-07-29 10:02:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-07-29 10:02:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-29 10:03:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-07-29 10:03:58</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07-29 10:04:28</td>\n",
       "      <td>113.797464</td>\n",
       "      <td>22.688276</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8554</th>\n",
       "      <td>2024-08-01 15:28:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>7948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8555</th>\n",
       "      <td>2024-08-01 15:29:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>7949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8556</th>\n",
       "      <td>2024-08-01 15:29:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>7950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8557</th>\n",
       "      <td>2024-08-01 15:30:20</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>7951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>2024-08-01 15:30:50</td>\n",
       "      <td>113.943112</td>\n",
       "      <td>22.688810</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8559 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     定位时间          经度         纬度  labels\n",
       "0     2024-07-29 10:02:28  113.797464  22.688276      -1\n",
       "1     2024-07-29 10:02:58  113.797464  22.688276      -1\n",
       "2     2024-07-29 10:03:28  113.797464  22.688276      -1\n",
       "3     2024-07-29 10:03:58  113.797464  22.688276      -1\n",
       "4     2024-07-29 10:04:28  113.797464  22.688276      -1\n",
       "...                   ...         ...        ...     ...\n",
       "8554  2024-08-01 15:28:50  113.943112  22.688810    7948\n",
       "8555  2024-08-01 15:29:20  113.943112  22.688810    7949\n",
       "8556  2024-08-01 15:29:50  113.943112  22.688810    7950\n",
       "8557  2024-08-01 15:30:20  113.943112  22.688810    7951\n",
       "8558  2024-08-01 15:30:50  113.943112  22.688810       4\n",
       "\n",
       "[8559 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a3287bc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(point[:, 0], point[:, 1], c=data['labels'], cmap='rainbow')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b2063bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存数据\n",
    "# data.to_excel(\"D:/data/0280jvlei.xlsx\",index=True,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059a2d33",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
